In [2]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import keras
from keras import Sequential
import re
from keras import layers
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Input
from tensorflow.keras.optimizers import Adam
In [42]:
#将数据切割为hobbies, foods, and household,并再分为train, validation and test,已经不需要切割了,即不用运行了
def split_data(category):  
    df                = pd.read_csv('train_with_price.csv')
    category_data     = df[df['cat_id'] == category]
    train_df, temp_df = train_test_split(category_data, test_size=0.2, random_state=0)
    test_df, val_df   = train_test_split(temp_df, test_size=0.5, random_state=0)
    train_df.to_csv(f'{category}_train_dataset.csv', index=False)
    test_df.to_csv (f'{category}_test_dataset.csv', index=False)
    val_df.to_csv  (f'{category}_validation_dataset.csv', index=False)

def split_all_data():
    category=['HOUSEHOLD','HOBBIES','FOODS']
    for x in category:
        split_data(x)
split_all_data()
C:\Users\a8090\AppData\Local\Temp\ipykernel_17932\1529081861.py:3: DtypeWarning: Columns (15,16) have mixed types. Specify dtype option on import or set low_memory=False.
  df                = pd.read_csv('train_with_price.csv')
C:\Users\a8090\AppData\Local\Temp\ipykernel_17932\1529081861.py:3: DtypeWarning: Columns (15,16) have mixed types. Specify dtype option on import or set low_memory=False.
  df                = pd.read_csv('train_with_price.csv')
C:\Users\a8090\AppData\Local\Temp\ipykernel_17932\1529081861.py:3: DtypeWarning: Columns (15,16) have mixed types. Specify dtype option on import or set low_memory=False.
  df                = pd.read_csv('train_with_price.csv')
In [3]:
#这里要手动把household改成foods和hobbies
train_data      = pd.read_csv("HOUSEHOLD_train_dataset.csv")
validation_data = pd.read_csv("HOUSEHOLD_validation_dataset.csv")
testing_data    = pd.read_csv("HOUSEHOLD_test_dataset.csv")

#预处理数据-去除不需要的栏,并将数据全部变为数字----
def preprocess_data(data):
    data['item_id']   = data['item_id'].astype(str).apply(lambda x: ''.join(re.findall(r'\d+', x)))
    data['dept_id']   = data['dept_id'].astype(str).apply(lambda x: ''.join(re.findall(r'\d+', x)))
    data.drop(['cat_id'], axis=1, inplace=True)
    data['store_id']  = data['store_id'].astype(str).apply(lambda x: ''.join(re.findall(r'\d+', x)))
    data['state_id']  = data['state_id'].replace({'CA': 1, 'WI': 2, 'TX': 3})
    data['d']         = data['d'].astype(str).apply(lambda x: ''.join(re.findall(r'\d+', x)))
    data.drop(['date'], axis=1, inplace=True)
    data['weekday']   = data['weekday'].replace({'Monday': 1, 'Tuesday': 2, 'Wednesday': 3, 'Thursday': 4, 'Friday': 5 , 'Saturday': 6, 'Sunday': 7})
    data.drop(['event_name_1'], axis=1, inplace=True)
    data.drop(['event_name_2'], axis=1, inplace=True)
    data['event_type_1'] = data['event_type_1'].replace({'Religious': 1, 'Cultural': 1, 'National': 1, 'Sporting': 1})
    data['event_type_2'] = data['event_type_2'].replace({'Religious': 1, 'Cultural': 1, 'National': 1, 'Sporting': 1})
    data['event_type_1'] = data['event_type_1'].fillna(0)   #将活动中空的数据变为0
    data['event_type_2'] = data['event_type_2'].fillna(0)
    data['event_type']   = data['event_type_1']+data['event_type_2']
    data.drop(['event_type_1'], axis=1, inplace=True)
    data.drop(['event_type_2'], axis=1, inplace=True)
    data = data.dropna()
    
preprocess_data(train_data)
preprocess_data(validation_data)
preprocess_data(testing_data)
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:12: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  data['state_id']  = data['state_id'].replace({'CA': 1, 'WI': 2, 'TX': 3})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:15: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  data['weekday']   = data['weekday'].replace({'Monday': 1, 'Tuesday': 2, 'Wednesday': 3, 'Thursday': 4, 'Friday': 5 , 'Saturday': 6, 'Sunday': 7})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:18: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  data['event_type_1'] = data['event_type_1'].replace({'Religious': 1, 'Cultural': 1, 'National': 1, 'Sporting': 1})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:19: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  data['event_type_2'] = data['event_type_2'].replace({'Religious': 1, 'Cultural': 1, 'National': 1, 'Sporting': 1})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:12: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  data['state_id']  = data['state_id'].replace({'CA': 1, 'WI': 2, 'TX': 3})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:15: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  data['weekday']   = data['weekday'].replace({'Monday': 1, 'Tuesday': 2, 'Wednesday': 3, 'Thursday': 4, 'Friday': 5 , 'Saturday': 6, 'Sunday': 7})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:18: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  data['event_type_1'] = data['event_type_1'].replace({'Religious': 1, 'Cultural': 1, 'National': 1, 'Sporting': 1})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:19: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  data['event_type_2'] = data['event_type_2'].replace({'Religious': 1, 'Cultural': 1, 'National': 1, 'Sporting': 1})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:12: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  data['state_id']  = data['state_id'].replace({'CA': 1, 'WI': 2, 'TX': 3})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:15: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  data['weekday']   = data['weekday'].replace({'Monday': 1, 'Tuesday': 2, 'Wednesday': 3, 'Thursday': 4, 'Friday': 5 , 'Saturday': 6, 'Sunday': 7})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:18: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  data['event_type_1'] = data['event_type_1'].replace({'Religious': 1, 'Cultural': 1, 'National': 1, 'Sporting': 1})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:19: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  data['event_type_2'] = data['event_type_2'].replace({'Religious': 1, 'Cultural': 1, 'National': 1, 'Sporting': 1})
In [4]:
train_data.head()
Out[4]:
id item_id dept_id store_id state_id d num_sold wm_yr_wk weekday month year snap sell_price event_type
0 HOUSEHOLD_1_225_WI_3_validation 1225 1 3 2 1422 3 11447 6 12 2014 0 1.64 0.0
1 HOUSEHOLD_1_523_TX_3_validation 1523 1 3 3 918 0 11328 6 8 2013 1 20.53 0.0
2 HOUSEHOLD_1_248_TX_3_validation 1248 1 3 3 1070 1 11349 4 1 2014 0 8.97 0.0
3 HOUSEHOLD_1_538_WI_1_validation 1538 1 1 2 490 1 11218 5 6 2012 0 3.48 0.0
4 HOUSEHOLD_2_261_TX_1_validation 2261 2 1 3 383 0 11203 3 2 2012 1 6.97 0.0

The val_df and eval_df above are in the requested format for Kaggle competition predictions. In the end, we will merge these dataframes into a final consolidated file.

In [ ]:
 
In [35]:
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Input, Dropout
from tensorflow.keras.optimizers import Adam
from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
import matplotlib.pyplot as plt
from tensorflow.keras.callbacks import EarlyStopping

def create_xy_data(df, pre_type = ""):
    x_train_id = (df['id'] + "_" + pre_type).values
    idx = np.unique(x_train_id, return_index=True)[1]
    idx.sort()
    x_train_id = x_train_id[idx]
    y_train = df['num_sold'].values
    df = df.drop(['id','num_sold','item_id', 'dept_id','wm_yr_wk', 'year'],axis=1)
    return df, y_train, x_train_id

def create_x(df, pre_type=""):
    x_train_id = (df['id'] + "_" + pre_type).values
    idx = np.unique(x_train_id, return_index=True)[1]
    idx.sort()
    x_train_id = x_train_id[idx]
    df = df.drop(['id','num_sold','item_id', 'dept_id', 'wm_yr_wk','year'],axis=1)
    return df, x_train_id

def create_time_steps(length):
    time_steps = []
    for i in range(-length, 0, 1):
        time_steps.append(i)
    return time_steps

def show_plot(plot_data, delta, title):
    labels = ['History', 'True Future', 'Model Prediction']
    marker = ['.-', 'rx', 'go']
    time_steps = create_time_steps(plot_data[0].shape[0])

    if delta:
        future = delta
    else:
        future = 0

    plt.title(title)
    for i, x in enumerate(plot_data):
        if i:
            plt.plot(future, plot_data[i], marker[i], markersize=10, label=labels[i])
        else:
            plt.plot(time_steps, plot_data[i].flatten(), marker[i], label=labels[i])
    plt.legend()
    plt.xlim([time_steps[0] - 1, (future + 2) * 2])
    plt.xlabel('Time-Step')
    return plt

LOOKBACK_MAX     = 28  #28? 14?
LOOKBACK_ARR     = np.array([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14])

y_val_pre_all     = []
y_eval_pre_all    = []
y_val_true_all    = []
val_id_all        = []
eval_id_all       = []
SE_list           = [] 
number_of_trained = 0
y_true_all    = []
y_pred_all    = []
unique_items  = train_data['item_id'].unique()
target_scaler = MinMaxScaler(feature_range=(0, 1))

y_eval_output = []

# create a model for each product
while number_of_trained < 100 and number_of_trained < len(unique_items):
    item = unique_items[number_of_trained]
    print("-----------------------------------")
    print("Current item is ", item)

    # Filter data for the current item
    item_train_data = train_data[train_data['item_id'] == item].reset_index(drop=True)
    item_val_data     = validation_data[validation_data['item_id'] == item].reset_index(drop=True)
    item_eval_data    = testing_data[testing_data['item_id'] == item].reset_index(drop=True)

    item_train_data['state_id'] = item_train_data['state_id'].astype(str)
    item_train_data['store_id'] = item_train_data['store_id'].astype(str)

    item_val_data['state_id'] = item_val_data['state_id'].astype(str)
    item_val_data['store_id'] = item_val_data['store_id'].astype(str)

    item_eval_data['state_id'] = item_eval_data['state_id'].astype(str)
    item_eval_data['store_id'] = item_eval_data['store_id'].astype(str)

    # Since it is not reasonable to use data from different stores when extracting the previous 14 days
    state_store_list         = (item_train_data['state_id'] + '_' + item_train_data['store_id']).to_numpy()
    val_state_store_list = (item_val_data['state_id'] + '_' + item_val_data['store_id']).to_numpy()
    eval_state_store_list = (item_eval_data['state_id'] + '_' + item_eval_data['store_id']).to_numpy()

    # Transform categorical data using One-hot-encoding
    train_dummy         = pd.get_dummies(item_train_data, columns=['store_id','state_id', 'weekday', 'snap'], drop_first=False)
    train_columns         = train_dummy.columns
    val_dummy           = pd.get_dummies(item_val_data,     columns=['store_id','state_id', 'weekday', 'snap'], drop_first=False)
    eval_dummy          = pd.get_dummies(item_eval_data,    columns=['store_id','state_id', 'weekday', 'snap'], drop_first=False)
    print("Data types of train_dummy after one-hot encoding:")
    print(train_dummy.dtypes)

    missing_cols = set(train_dummy.columns) - set(eval_dummy.columns)
    for col in missing_cols:
        eval_dummy[col] = 0  # 补全缺失的列,并设为0
    eval_dummy = eval_dummy[train_dummy.columns]  # 保持列顺序一致
    eval_dummy = eval_dummy.reindex(columns=train_dummy.columns, fill_value=0)
    # Create dataframe
    x_data, y_data, data_id = create_xy_data(train_dummy)
    x_val, y_val, val_id     = create_xy_data(val_dummy, pre_type="validation")
    x_eval, eval_id          = create_x(eval_dummy, pre_type="evaluation")

    val_id_all.append(val_id)
    eval_id_all.append(eval_id)

    # Standardize price
    scaler_x = StandardScaler()
    x_data['sell_price']     = scaler_x.fit_transform(np.array(x_data['sell_price']).reshape(-1,1))
    x_val['sell_price']       = scaler_x.transform(np.array(x_val['sell_price']).reshape(-1,1))
    x_eval['sell_price']    = scaler_x.transform(np.array(x_eval['sell_price']).reshape(-1,1))
    x_columns = x_data.columns
    print(x_columns)

    # Scale target variable
    print(f"Original y_data range: min={np.min(y_data)}, max={np.max(y_data)}")
    y_data_scaled = target_scaler.fit_transform(y_data.reshape(-1, 1)).flatten()
    print(f"Scaled y_data range: min={np.min(y_data_scaled)}, max={np.max(y_data_scaled)}")
    y_val_scaled = target_scaler.transform(y_val.reshape(-1, 1)).flatten()
    print(f"Scaled y_val range: min={np.min(y_val_scaled)}, max={np.max(y_val_scaled)}")

    x_time = []
    y_time = []
    x_val_time = []
    y_val_time = []
    x_eval_time = []
    i = LOOKBACK_MAX

    # Store-specific data extraction and handling
    unique_stores = np.unique(state_store_list)
    for store in unique_stores:
        train_indices = np.where(state_store_list == store)[0]
        val_indices   = np.where(val_state_store_list == store)[0]
        eval_indices  = np.where(eval_state_store_list == store)[0]

        store_x_data = x_data.iloc[train_indices].reset_index(drop=True)
        store_y_data = y_data_scaled[train_indices]
        store_x_val  = x_val.iloc[val_indices].reset_index(drop=True)
        store_y_val  = y_val_scaled[val_indices]
        store_x_eval = x_eval.iloc[eval_indices].reset_index(drop=True)

        # Convert boolean columns to integers
        bool_cols = ['store_id_1', 'store_id_2', 'store_id_3', 'store_id_4',
                     'state_id_1', 'state_id_2', 'state_id_3',
                     'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5', 'weekday_6', 'weekday_7',
                     'snap_0', 'snap_1']
        for col in bool_cols:
            if col in store_x_data.columns:
                store_x_data[col] = store_x_data[col].astype(int)
            if col in store_x_val.columns:
                store_x_val[col] = store_x_val[col].astype(int)
            if col in store_x_eval.columns:
                store_x_eval[col] = store_x_eval[col].astype(int)

        #看看有没有问题
        for col in ['event_type_1', 'event_type_2']:
            if col in store_x_data.columns:
                store_x_data[col] = pd.to_numeric(store_x_data[col], errors='coerce')
            if col in store_x_val.columns:
                store_x_val[col] = pd.to_numeric(store_x_val[col], errors='coerce')
            if col in store_x_eval.columns:
                store_x_eval[col] = pd.to_numeric(store_x_eval[col], errors='coerce')

        # Process training data for lookback
        for i in range(LOOKBACK_MAX, len(store_x_data)):
            lookback_data = []
            for lb in LOOKBACK_ARR:
                if i - lb >= 0:
                    lookback_data.append(store_x_data.iloc[i - lb].values.tolist())
                else:
                    lookback_data.append([0] * store_x_data.shape[1])
            x_time.append(lookback_data)
            y_time.append(store_y_data[i])

        # Process validation data for lookback
        for i in range(LOOKBACK_MAX, len(store_x_val)):
            lookback_data = []
            for lb in LOOKBACK_ARR:
                if i - lb >= 0:
                    lookback_data.append(store_x_val.iloc[i - lb].values.tolist())
                else:
                    lookback_data.append([0] * store_x_val.shape[1])
            x_val_time.append(lookback_data)
            y_val_time.append(store_y_val[i])

        for i in range(LOOKBACK_MAX, len(store_x_eval)):
            lookback_data = []
            for lb in LOOKBACK_ARR:
                if i - lb >= 0:
                    lookback_data.append(store_x_eval.iloc[i - lb].values.tolist())
                else:
                    lookback_data.append([0] * store_x_eval.shape[1])
            x_eval_time.append(lookback_data)

    x_time     = np.array(x_time).astype(np.float32)
    y_time     = np.array(y_time).astype(np.float32)
    x_val_time = np.array(x_val_time).astype(np.float32)
    y_val_time = np.array(y_val_time).astype(np.float32)
    x_eval_time= np.array(x_eval_time).astype(np.float32)


    print(f"x_eval_time shape before reshape: {x_eval_time.shape}")
    print(f"x_data.shape[1]: {x_data.shape[1]}")
    print(f"LOOKBACK_ARR.shape[0]: {LOOKBACK_ARR.shape[0]}")
    print(f"item_eval_data.shape: {item_eval_data.shape}")
    unique_stores = np.unique(eval_state_store_list)
    for store in unique_stores:
        eval_indices = np.where(eval_state_store_list == store)[0]
        store_x_eval = x_eval.iloc[eval_indices].reset_index(drop=True)
        print(f"store_x_eval.shape: {store_x_eval.shape}")


    x_time = x_time.reshape((x_time.shape[0], LOOKBACK_ARR.shape[0], x_data.shape[1]))
    x_val_time = x_val_time.reshape((x_val_time.shape[0], LOOKBACK_ARR.shape[0], x_data.shape[1]))
    x_eval_time = x_eval_time.reshape((x_eval_time.shape[0], LOOKBACK_ARR.shape[0], x_eval.shape[1]))

    model = Sequential()
    model.add(Input(shape=(LOOKBACK_ARR.shape[0], x_data.shape[1])))
    model.add(LSTM(256, activation='relu', return_sequences=True))
    model.add(Dropout(0.2))
    model.add(LSTM(256, activation='relu'))
    model.add(Dropout(0.2))
    model.add(Dense(1))
    model.compile(optimizer=Adam(learning_rate=0.001), loss='mse')
    model.summary()

    number_of_trained +=1

    # Early stopping
    early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)

    history = model.fit(x = x_time,
                        y = y_time,
                        epochs=100,
                        shuffle=True,
                        batch_size=128,
                        validation_split = 0.1,
                        verbose=1,
                        callbacks=[early_stopping])
    # Predict
    y_val_pre_scaled = model.predict(x_val_time).reshape(-1,)
    y_val_pre = target_scaler.inverse_transform(y_val_pre_scaled.reshape(-1, 1)).flatten()
    y_val_true_original = target_scaler.inverse_transform(y_val_time.reshape(-1, 1)).flatten()
    y_val_pre = np.maximum(0, y_val_pre)

    y_val_pre_all.extend(y_val_pre)
    y_val_true_all.extend(y_val_true_original)
    print("Sample raw predictions (after inverse transform and clipping):", y_val_pre[:5])
    MSE = mean_squared_error(y_val_true_original, y_val_pre)
    SE_list.append(MSE*len(y_val_pre))
    print("RMSE = ", np.sqrt(MSE))

    r2 = r2_score(y_val_true_original, y_val_pre)
    print(f"Validation R-squared for item {item}: {r2}")

    y_eval_pre_scaled = model.predict(x_eval_time).reshape(-1,)
    y_eval_pre = target_scaler.inverse_transform(y_eval_pre_scaled.reshape(-1, 1)).flatten()
    y_eval_pre = np.maximum(0, y_eval_pre)
    if len(y_eval_pre) >= 28:
        y_eval_output.append(y_eval_pre[-28:])
    else:
        padding = np.zeros(28 - len(y_eval_pre))
        y_eval_output.append(np.concatenate([y_eval_pre, padding]))

    print(f"Predicted y_val range (after inverse transform and clipping): min={np.min(y_val_pre)}, max={np.max(y_val_pre)}")
    print(f"True y_val range (after inverse transform): min={np.min(y_val_true_original)}, max={np.max(y_val_true_original)}")

    #可视化图表-折线图
    plot_idx = np.random.randint(0, len(x_val_time))
    plot_sample = x_val_time[plot_idx]
    true_value_scaled = y_val_time[plot_idx]
    prediction_scaled = y_val_pre_scaled[plot_idx]

    true_value_original = target_scaler.inverse_transform(np.array([true_value_scaled]).reshape(-1, 1)).flatten()[0]
    prediction_original = target_scaler.inverse_transform(np.array([prediction_scaled]).reshape(-1, 1)).flatten()[0]

    plot_data = [
        plot_sample[:, -1],
        np.array([true_value_original]),
        np.array([prediction_original])]
    plt = show_plot(plot_data, delta=1, title=f'Item {item} Prediction Example')
    plt.show()

from sklearn.metrics import r2_score, mean_absolute_error
#给出总体的结果
overall_mse = mean_squared_error(y_val_true_all, y_val_pre_all)
overall_rmse = np.sqrt(overall_mse)
overall_r2 = r2_score(y_val_true_all, y_val_pre_all)
overall_mae = mean_absolute_error(y_val_true_all, y_val_pre_all)
print("-----------------------------------")
print("Overall Validation MSE:", overall_mse)
print("Overall Validation RMSE:", overall_rmse)
print("Overall Validation MAE:", overall_mae)
print("Overall Validation R-squared:", overall_r2)
-----------------------------------
Current item is  1225
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=19
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7894736842105263
x_eval_time shape before reshape: (1653, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1933, 14)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (171, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (216, 20)
store_x_eval.shape: (169, 20)
Model: "sequential_76"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_153 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_58 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_154 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_59 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_75 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 53ms/step - loss: 18924.4102 - val_loss: 1972.5114
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 3934.3872 - val_loss: 43.5837
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 201.9400 - val_loss: 16.4313
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 107.3859 - val_loss: 15.2957
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 81.3130 - val_loss: 8.4849
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 58.3467 - val_loss: 5.5222
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 34.1280 - val_loss: 3.1466
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 22.7189 - val_loss: 1.6834
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 18.6499 - val_loss: 1.6412
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 14.3320 - val_loss: 1.2821
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 52ms/step - loss: 11.1413 - val_loss: 0.9112
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 8.2863 - val_loss: 1.4349
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 5.9132 - val_loss: 0.9281
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 4.5225 - val_loss: 0.1967
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 3.8019 - val_loss: 0.1853
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 3.4947 - val_loss: 0.6657
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 2.3398 - val_loss: 0.1178
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 52ms/step - loss: 2.0841 - val_loss: 0.1700
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 1.9809 - val_loss: 0.2252
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 1.6044 - val_loss: 0.4580
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 1.0813 - val_loss: 3.5423
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 1.5756 - val_loss: 0.0879
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 1.2094 - val_loss: 0.0547
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.7845 - val_loss: 0.8424
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 52ms/step - loss: 1.0836 - val_loss: 0.8386
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 1.0909 - val_loss: 0.3903
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.5830 - val_loss: 0.0453
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.6938 - val_loss: 0.4115
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.6884 - val_loss: 1.0772
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.6898 - val_loss: 1.6949
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.9349 - val_loss: 0.0234
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.4164 - val_loss: 0.2498
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.5154 - val_loss: 0.5085
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.9582 - val_loss: 0.0206
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.3980 - val_loss: 0.4075
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 50ms/step - loss: 0.4681 - val_loss: 0.1553
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.5845 - val_loss: 0.0316
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.2374 - val_loss: 7.0152
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 1.8323 - val_loss: 0.0333
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.1305 - val_loss: 0.7829
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.5019 - val_loss: 0.0767
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.6843 - val_loss: 0.1282
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.7374 - val_loss: 0.0664
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.2720 - val_loss: 0.0113
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.2611 - val_loss: 0.0128
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.3468 - val_loss: 0.6177
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.1939 - val_loss: 0.1886
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.3647 - val_loss: 0.1296
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.5314 - val_loss: 0.5724
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.2617 - val_loss: 0.7809
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.8595 - val_loss: 0.2279
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.3186 - val_loss: 0.0122
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.1582 - val_loss: 0.0532
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.1202 - val_loss: 0.8902
53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step 
Sample raw predictions (after inverse transform and clipping): [2.2722342 2.019139  2.2932293 0.        0.7174268]
RMSE =  2.5319178
Validation R-squared for item 1225: -0.0014235973358154297
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=4.185342311859131
True y_val range (after inverse transform): min=0.0, max=15.000000953674316
No description has been provided for this image
-----------------------------------
Current item is  1523
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=5
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.2000000000000002
x_eval_time shape before reshape: (1559, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1839, 14)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (167, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (210, 20)
store_x_eval.shape: (160, 20)
store_x_eval.shape: (178, 20)
Model: "sequential_77"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_155 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_60 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_156 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_61 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_76 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 7s 53ms/step - loss: 14375.5762 - val_loss: 675.8505
Epoch 2/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 996.1748 - val_loss: 17.9162
Epoch 3/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 112.1268 - val_loss: 2.4032
Epoch 4/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 48.4905 - val_loss: 1.3057
Epoch 5/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 32.1874 - val_loss: 0.5965
Epoch 6/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 20.3652 - val_loss: 0.4104
Epoch 7/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 11.1131 - val_loss: 0.2977
Epoch 8/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 6.4639 - val_loss: 0.1202
Epoch 9/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 4.1530 - val_loss: 0.0690
Epoch 10/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 2.6970 - val_loss: 0.0471
Epoch 11/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 1.6188 - val_loss: 0.0391
Epoch 12/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.1543 - val_loss: 0.0540
Epoch 13/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 1.3431 - val_loss: 0.0230
Epoch 14/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 1.0959 - val_loss: 0.0468
Epoch 15/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.9224 - val_loss: 0.0157
Epoch 16/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.6580 - val_loss: 0.0145
Epoch 17/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.5738 - val_loss: 0.0147
Epoch 18/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.5274 - val_loss: 0.0110
Epoch 19/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.4620 - val_loss: 0.0103
Epoch 20/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.3637 - val_loss: 0.0230
Epoch 21/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.3407 - val_loss: 0.0221
Epoch 22/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.2175 - val_loss: 0.0121
Epoch 23/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.1130 - val_loss: 0.0056
Epoch 24/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1146 - val_loss: 0.0054
Epoch 25/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0541 - val_loss: 0.0050
Epoch 26/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0573 - val_loss: 0.0066
Epoch 27/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0534 - val_loss: 0.0047
Epoch 28/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0540 - val_loss: 0.0048
Epoch 29/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0427 - val_loss: 0.0049
Epoch 30/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0764 - val_loss: 0.0051
Epoch 31/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0483 - val_loss: 0.0051
Epoch 32/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0648 - val_loss: 0.0047
Epoch 33/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0246 - val_loss: 0.0048
Epoch 34/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0386 - val_loss: 0.0118
Epoch 35/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 12.8230 - val_loss: 0.4015
Epoch 36/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 3.0136 - val_loss: 0.0045
Epoch 37/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.1632 - val_loss: 0.0050
Epoch 38/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.1161 - val_loss: 0.0044
Epoch 39/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0579 - val_loss: 0.0043
Epoch 40/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0582 - val_loss: 0.0045
Epoch 41/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0239 - val_loss: 0.0044
Epoch 42/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0651 - val_loss: 0.0043
Epoch 43/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0188 - val_loss: 0.0043
Epoch 44/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0175 - val_loss: 0.0043
Epoch 45/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0541 - val_loss: 0.0043
Epoch 46/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0286 - val_loss: 0.0043
Epoch 47/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1814 - val_loss: 0.0042
Epoch 48/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0188 - val_loss: 0.0045
Epoch 49/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0136 - val_loss: 0.0043
Epoch 50/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0096 - val_loss: 0.0043
Epoch 51/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0328 - val_loss: 0.0043
Epoch 52/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0077 - val_loss: 0.0043
Epoch 53/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0100 - val_loss: 0.0043
Epoch 54/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0372 - val_loss: 0.0043
Epoch 55/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0153 - val_loss: 0.0043
Epoch 56/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0153 - val_loss: 0.0043
Epoch 57/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0084 - val_loss: 0.0043
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step 
Sample raw predictions (after inverse transform and clipping): [0.         0.06638031 0.         0.07186427 0.07335408]
RMSE =  0.39316848
Validation R-squared for item 1523: 0.008431077003479004
49/49 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.07957331091165543
True y_val range (after inverse transform): min=0.0, max=6.0
No description has been provided for this image
-----------------------------------
Current item is  1248
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=8
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.875
x_eval_time shape before reshape: (1660, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1940, 14)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (206, 20)
Model: "sequential_78"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_157 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_62 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_158 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_63 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_77 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 54ms/step - loss: 24026.9473 - val_loss: 793.8290
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 2744.6355 - val_loss: 227.1237
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 855.1089 - val_loss: 165.0681
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 477.7781 - val_loss: 71.7934
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 172.5361 - val_loss: 30.8414
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 64.1932 - val_loss: 13.4713
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 19.3592 - val_loss: 0.5412
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 3.0346 - val_loss: 0.3821
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.9311 - val_loss: 0.2638
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 1.4099 - val_loss: 0.2623
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.0135 - val_loss: 0.2730
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.9153 - val_loss: 0.1904
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.6460 - val_loss: 0.4742
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.6965 - val_loss: 0.1564
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.5279 - val_loss: 0.2865
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.6146 - val_loss: 0.1373
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.4497 - val_loss: 0.1310
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.4309 - val_loss: 0.2612
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.4116 - val_loss: 0.2119
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.4244 - val_loss: 0.1989
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.3667 - val_loss: 0.4144
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.4053 - val_loss: 0.2122
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.4487 - val_loss: 0.5739
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.4119 - val_loss: 0.0968
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.3300 - val_loss: 0.0961
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.3216 - val_loss: 0.0852
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.4113 - val_loss: 0.0798
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.2622 - val_loss: 0.2852
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.3576 - val_loss: 0.1758
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.2142 - val_loss: 0.1538
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.2382 - val_loss: 0.0697
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.2233 - val_loss: 0.2451
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.2406 - val_loss: 0.1461
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1955 - val_loss: 0.2066
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.2139 - val_loss: 0.0686
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1908 - val_loss: 0.0808
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1678 - val_loss: 0.0456
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1854 - val_loss: 0.0457
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1759 - val_loss: 0.0448
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1561 - val_loss: 0.1248
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1711 - val_loss: 0.3891
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.1809 - val_loss: 0.0876
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1344 - val_loss: 0.0345
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1402 - val_loss: 0.1005
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1627 - val_loss: 0.0338
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1302 - val_loss: 0.0864
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0980 - val_loss: 0.2093
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1356 - val_loss: 0.1138
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0979 - val_loss: 0.2162
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1157 - val_loss: 0.0261
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0930 - val_loss: 0.0217
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0704 - val_loss: 0.0239
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0611 - val_loss: 0.0202
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0737 - val_loss: 0.0196
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0581 - val_loss: 0.0177
Epoch 56/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0690 - val_loss: 0.0359
Epoch 57/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0670 - val_loss: 0.0277
Epoch 58/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1516 - val_loss: 0.0155
Epoch 59/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0660 - val_loss: 0.0209
Epoch 60/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0509 - val_loss: 0.0541
Epoch 61/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0466 - val_loss: 0.0183
Epoch 62/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0356 - val_loss: 0.0173
Epoch 63/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0775 - val_loss: 0.0128
Epoch 64/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0365 - val_loss: 0.0122
Epoch 65/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0307 - val_loss: 0.0262
Epoch 66/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0337 - val_loss: 0.0597
Epoch 67/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0475 - val_loss: 0.0115
Epoch 68/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0397 - val_loss: 0.0183
Epoch 69/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0246 - val_loss: 0.0311
Epoch 70/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0360 - val_loss: 0.0124
Epoch 71/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0324 - val_loss: 0.0105
Epoch 72/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0438 - val_loss: 0.0107
Epoch 73/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0254 - val_loss: 0.0389
Epoch 74/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0431 - val_loss: 0.0109
Epoch 75/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0488 - val_loss: 0.0226
Epoch 76/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0607 - val_loss: 0.0149
Epoch 77/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0392 - val_loss: 0.0115
Epoch 78/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.0589 - val_loss: 0.0888
Epoch 79/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.0521 - val_loss: 0.0112
Epoch 80/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0337 - val_loss: 0.0117
Epoch 81/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0159 - val_loss: 0.0112
49/49 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step 
Sample raw predictions (after inverse transform and clipping): [0.63034356 0.5797154  0.54396343 0.6172495  0.6111356 ]
RMSE =  0.9929343
Validation R-squared for item 1248: 0.03697305917739868
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.031778335571289
True y_val range (after inverse transform): min=0.0, max=7.0
No description has been provided for this image
-----------------------------------
Current item is  1538
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=14
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8571428571428571
x_eval_time shape before reshape: (1623, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1903, 14)
store_x_eval.shape: (172, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (213, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (186, 20)
Model: "sequential_79"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_159 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_64 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_160 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_65 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_78 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 55ms/step - loss: 41959.0703 - val_loss: 2031.2432
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 6123.6948 - val_loss: 969.4879
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1905.3384 - val_loss: 406.2067
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 984.2078 - val_loss: 456.9229
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 721.8736 - val_loss: 34.0741
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 146.9604 - val_loss: 12.6738
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 52.8940 - val_loss: 15.0009
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 36.6514 - val_loss: 4.2288
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 20.1532 - val_loss: 2.2451
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 13.7341 - val_loss: 2.9157
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 15.5807 - val_loss: 1.3909
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 9.9564 - val_loss: 1.0309
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 8.0590 - val_loss: 1.4787
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 7.4627 - val_loss: 1.5446
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 8.0416 - val_loss: 0.7313
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 5.2434 - val_loss: 0.5404
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 4.7093 - val_loss: 0.4622
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 4.5738 - val_loss: 0.3846
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 4.1706 - val_loss: 1.7314
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 3.9720 - val_loss: 0.4521
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 3.6575 - val_loss: 1.5607
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 3.1649 - val_loss: 0.2225
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 3.4060 - val_loss: 0.1982
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 2.7193 - val_loss: 0.2320
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 2.9729 - val_loss: 0.1875
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 2.6760 - val_loss: 1.4319
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 2.7769 - val_loss: 0.1552
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 2.2377 - val_loss: 0.3007
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 2.1522 - val_loss: 0.1516
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 2.1330 - val_loss: 0.3162
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.9306 - val_loss: 0.1074
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 2.3067 - val_loss: 0.4346
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 1.8774 - val_loss: 1.5859
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 2.0826 - val_loss: 0.4320
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.8505 - val_loss: 0.6556
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 1.6403 - val_loss: 0.2080
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.6060 - val_loss: 0.5062
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.4251 - val_loss: 0.1631
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.1662 - val_loss: 0.1030
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.3284 - val_loss: 0.0737
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.2199 - val_loss: 0.5149
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.5666 - val_loss: 0.6006
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.2929 - val_loss: 0.0626
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.1791 - val_loss: 0.3582
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.4624 - val_loss: 0.0588
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.6098 - val_loss: 0.4639
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.3828 - val_loss: 0.1437
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.2103 - val_loss: 0.0691
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 1.0930 - val_loss: 0.1633
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.3568 - val_loss: 0.1975
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.1776 - val_loss: 0.1003
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.1255 - val_loss: 0.5911
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.0996 - val_loss: 0.1483
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.9968 - val_loss: 0.1230
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.9849 - val_loss: 0.2787
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step 
Sample raw predictions (after inverse transform and clipping): [2.5119102 2.2433257 4.408477  1.2089206 2.584147 ]
RMSE =  2.817949
Validation R-squared for item 1538: -3.216616630554199
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=11.389867782592773
True y_val range (after inverse transform): min=0.0, max=12.0
No description has been provided for this image
-----------------------------------
Current item is  2261
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=6
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.0
x_eval_time shape before reshape: (1609, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1889, 14)
store_x_eval.shape: (213, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (152, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (202, 20)
Model: "sequential_80"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_161 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_66 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_162 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_67 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_79 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 7s 56ms/step - loss: 18356.6504 - val_loss: 1372.9249
Epoch 2/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 2096.1243 - val_loss: 151.5039
Epoch 3/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 265.0948 - val_loss: 101.6968
Epoch 4/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 266.2577 - val_loss: 24.5295
Epoch 5/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 101.3274 - val_loss: 19.5663
Epoch 6/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 77.3651 - val_loss: 10.7925
Epoch 7/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 42.1672 - val_loss: 9.7049
Epoch 8/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 25.9676 - val_loss: 8.7867
Epoch 9/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 16.0472 - val_loss: 4.5568
Epoch 10/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 12.3033 - val_loss: 7.1910
Epoch 11/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 10.7089 - val_loss: 3.0900
Epoch 12/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 8.4570 - val_loss: 1.6945
Epoch 13/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 6.4940 - val_loss: 1.2182
Epoch 14/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 9.6205 - val_loss: 7.5517
Epoch 15/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 10.8824 - val_loss: 1.5250
Epoch 16/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 6.3326 - val_loss: 1.2789
Epoch 17/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 4.9424 - val_loss: 0.4380
Epoch 18/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 2.6740 - val_loss: 0.3503
Epoch 19/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.4201 - val_loss: 0.2654
Epoch 20/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.9481 - val_loss: 1.9325
Epoch 21/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 4.8880 - val_loss: 0.4248
Epoch 22/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.9355 - val_loss: 0.2256
Epoch 23/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.0352 - val_loss: 0.1608
Epoch 24/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.8741 - val_loss: 0.1429
Epoch 25/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.3140 - val_loss: 0.0766
Epoch 26/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.1957 - val_loss: 1.3173
Epoch 27/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.4619 - val_loss: 0.0341
Epoch 28/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1493 - val_loss: 0.0249
Epoch 29/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1847 - val_loss: 0.2003
Epoch 30/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1421 - val_loss: 0.0354
Epoch 31/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1015 - val_loss: 0.0720
Epoch 32/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.0748 - val_loss: 0.0163
Epoch 33/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1100 - val_loss: 0.0272
Epoch 34/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.0928 - val_loss: 0.0675
Epoch 35/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1828 - val_loss: 0.0134
Epoch 36/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0890 - val_loss: 0.0953
Epoch 37/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1172 - val_loss: 0.0225
Epoch 38/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.1955 - val_loss: 0.0995
Epoch 39/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.1436 - val_loss: 0.2400
Epoch 40/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.2166 - val_loss: 0.0161
Epoch 41/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0769 - val_loss: 0.0164
Epoch 42/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0889 - val_loss: 0.0125
Epoch 43/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.0797 - val_loss: 0.0812
Epoch 44/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.4213 - val_loss: 0.0300
Epoch 45/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.2132 - val_loss: 0.0223
Epoch 46/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.0466 - val_loss: 0.0190
Epoch 47/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.0416 - val_loss: 0.0355
Epoch 48/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.0323 - val_loss: 0.0599
Epoch 49/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.0400 - val_loss: 0.0586
Epoch 50/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0558 - val_loss: 0.0198
Epoch 51/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.3228 - val_loss: 0.2836
Epoch 52/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.7146 - val_loss: 0.0880
50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step 
Sample raw predictions (after inverse transform and clipping): [0.         0.01364818 0.03765383 0.18379766 0.        ]
RMSE =  0.7404233
Validation R-squared for item 2261: -0.5214437246322632
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=11.389328956604004
True y_val range (after inverse transform): min=0.0, max=6.0
No description has been provided for this image
-----------------------------------
Current item is  2103
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=8
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.75
x_eval_time shape before reshape: (1593, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1873, 14)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (176, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (168, 20)
store_x_eval.shape: (215, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (180, 20)
Model: "sequential_81"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_163 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_68 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_164 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_69 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_80 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 57ms/step - loss: 23160.5195 - val_loss: 3007.2212
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 4080.3223 - val_loss: 106.1965
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 342.3623 - val_loss: 29.9898
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 118.4812 - val_loss: 14.5527
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 77.4847 - val_loss: 2.6997
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 12.0562 - val_loss: 2.8802
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 10.6096 - val_loss: 0.1044
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 1.0678 - val_loss: 0.0187
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.5533 - val_loss: 0.0227
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.3805 - val_loss: 0.0251
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.4120 - val_loss: 0.0463
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.2318 - val_loss: 0.0227
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.1730 - val_loss: 0.0170
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.1774 - val_loss: 0.0119
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 0.1156 - val_loss: 0.0153
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 0.1358 - val_loss: 0.0426
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.1595 - val_loss: 0.0044
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.1054 - val_loss: 0.0555
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.1250 - val_loss: 0.0287
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.1014 - val_loss: 0.0107
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 0.1747 - val_loss: 0.0109
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.1780 - val_loss: 0.0569
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.0589 - val_loss: 0.0660
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 0.1236 - val_loss: 0.0150
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.1161 - val_loss: 0.0573
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.0848 - val_loss: 0.0269
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 0.0940 - val_loss: 0.0182
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step 
Sample raw predictions (after inverse transform and clipping): [0.         0.         0.         0.         0.08365785]
RMSE =  0.62742025
Validation R-squared for item 2103: -0.3039228916168213
50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=6.63416862487793
True y_val range (after inverse transform): min=0.0, max=6.0
No description has been provided for this image
-----------------------------------
Current item is  2475
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5
x_eval_time shape before reshape: (1637, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1917, 14)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (176, 20)
Model: "sequential_82"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_165 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_70 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_166 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_71 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_81 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 59ms/step - loss: 9412.5967 - val_loss: 1445.9342
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 2485.5779 - val_loss: 34.6208
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 113.9763 - val_loss: 24.0422
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 71.7826 - val_loss: 13.7146
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 28.8629 - val_loss: 7.3721
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 16.3532 - val_loss: 2.0382
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 10.3375 - val_loss: 1.0634
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 7.5515 - val_loss: 0.3124
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 6.1591 - val_loss: 0.5202
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 4.5108 - val_loss: 0.3067
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 3.1653 - val_loss: 0.1350
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 2.3322 - val_loss: 0.0822
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 2.3853 - val_loss: 0.0724
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 2.0034 - val_loss: 0.0579
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 1.6176 - val_loss: 0.0412
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.8917 - val_loss: 0.0542
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.5413 - val_loss: 0.1785
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 8.3782 - val_loss: 0.0046
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 0.3461 - val_loss: 0.0104
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.1875 - val_loss: 0.0164
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.2140 - val_loss: 0.0033
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 0.1797 - val_loss: 0.0562
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.1451 - val_loss: 0.1814
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 0.6547 - val_loss: 0.0044
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.2284 - val_loss: 0.0046
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.8703 - val_loss: 16.1011
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 8.3775 - val_loss: 0.0050
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.4041 - val_loss: 0.0018
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0930 - val_loss: 0.0016
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 0.2635 - val_loss: 0.0028
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.1184 - val_loss: 0.0016
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.1084 - val_loss: 0.0016
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0506 - val_loss: 0.0015
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0625 - val_loss: 0.0015
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0665 - val_loss: 0.0016
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0686 - val_loss: 0.0015
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0427 - val_loss: 0.0015
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0468 - val_loss: 0.0015
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0946 - val_loss: 0.0015
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0618 - val_loss: 0.0015
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0334 - val_loss: 0.0020
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.1115 - val_loss: 0.0015
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0175 - val_loss: 0.0015
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0549 - val_loss: 0.0024
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0258 - val_loss: 0.0015
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0182 - val_loss: 0.0016
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0119 - val_loss: 0.0021
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step 
Sample raw predictions (after inverse transform and clipping): [0.21895541 0.19312853 0.2019254  0.07124845 0.07927851]
RMSE =  0.5988919
Validation R-squared for item 2475: -0.02789163589477539
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.3731420040130615
True y_val range (after inverse transform): min=0.0, max=5.0
No description has been provided for this image
-----------------------------------
Current item is  2359
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=6
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8333333333333333
x_eval_time shape before reshape: (1662, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1942, 14)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (210, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (209, 20)
store_x_eval.shape: (184, 20)
Model: "sequential_83"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_167 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_72 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_168 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_73 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_82 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 59ms/step - loss: 9365.9268 - val_loss: 190.7198
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 494.0857 - val_loss: 9.5514
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 73.7295 - val_loss: 3.4019
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 25.3000 - val_loss: 1.3396
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 23.3465 - val_loss: 0.9357
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 14.4071 - val_loss: 0.9817
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 10.2439 - val_loss: 0.4970
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 7.8465 - val_loss: 0.3834
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 5.3499 - val_loss: 0.2635
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 4.8258 - val_loss: 0.1938
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 3.7411 - val_loss: 0.1728
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 3.5044 - val_loss: 0.1127
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 2.6467 - val_loss: 0.2827
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 2.2827 - val_loss: 0.0975
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 1.8433 - val_loss: 0.0667
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 1.8669 - val_loss: 0.2220
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 1.6133 - val_loss: 0.3116
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 1.2587 - val_loss: 0.0684
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 1.1905 - val_loss: 0.1103
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 1.0209 - val_loss: 0.0911
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 1.0325 - val_loss: 0.0453
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.7883 - val_loss: 0.0235
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.7712 - val_loss: 0.1041
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.8371 - val_loss: 0.1095
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.6186 - val_loss: 0.0644
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.4686 - val_loss: 0.1097
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.4329 - val_loss: 0.0107
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.3530 - val_loss: 0.0622
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.3395 - val_loss: 0.2616
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.2787 - val_loss: 0.0143
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.3031 - val_loss: 0.0430
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.3093 - val_loss: 0.2819
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.3056 - val_loss: 0.3881
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.2185 - val_loss: 0.2442
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.2504 - val_loss: 0.0281
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.1827 - val_loss: 0.0144
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.1767 - val_loss: 0.0138
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step 
Sample raw predictions (after inverse transform and clipping): [0.4330304  0.13572162 0.07607672 0.08268166 0.51625425]
RMSE =  0.5034702
Validation R-squared for item 2359: -0.2726236581802368
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=2.137045383453369
True y_val range (after inverse transform): min=0.0, max=5.0
No description has been provided for this image
-----------------------------------
Current item is  1428
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=11
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7272727272727273
x_eval_time shape before reshape: (1660, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1940, 14)
store_x_eval.shape: (216, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (170, 20)
store_x_eval.shape: (198, 20)
Model: "sequential_84"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_169 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_74 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_170 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_75 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_83 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 61ms/step - loss: 38466.8789 - val_loss: 1101.1453
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 4987.2612 - val_loss: 172.3611
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 166.7888 - val_loss: 18.7164
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 55.3474 - val_loss: 2.4334
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 30.0404 - val_loss: 3.2688
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 19.2536 - val_loss: 0.5350
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 13.1905 - val_loss: 0.7582
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 9.1895 - val_loss: 1.1327
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 6.6861 - val_loss: 0.3294
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 4.8286 - val_loss: 0.1445
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 3.5145 - val_loss: 0.3638
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 2.9436 - val_loss: 0.0561
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 2.3528 - val_loss: 0.4365
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 1.9683 - val_loss: 0.0453
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 1.6058 - val_loss: 0.1601
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1.2498 - val_loss: 0.1273
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1.1708 - val_loss: 0.0190
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.8044 - val_loss: 0.3787
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.8620 - val_loss: 0.1376
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.6681 - val_loss: 0.0103
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.6015 - val_loss: 0.1685
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.5587 - val_loss: 0.0138
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.6405 - val_loss: 0.0086
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.4801 - val_loss: 0.2474
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.3793 - val_loss: 0.0061
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.3368 - val_loss: 0.0111
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.5011 - val_loss: 0.0058
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.3236 - val_loss: 0.3376
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.4763 - val_loss: 0.0726
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.3592 - val_loss: 0.0102
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.2727 - val_loss: 0.0611
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.3624 - val_loss: 0.0071
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.1940 - val_loss: 0.0762
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.3658 - val_loss: 0.0209
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.1960 - val_loss: 0.5810
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.8062 - val_loss: 0.3015
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.3248 - val_loss: 0.0356
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step 
Sample raw predictions (after inverse transform and clipping): [0.         0.         0.06808062 0.         0.        ]
RMSE =  0.97091347
Validation R-squared for item 1428: -0.3451046943664551
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=4.895780086517334
True y_val range (after inverse transform): min=0.0, max=8.0
No description has been provided for this image
-----------------------------------
Current item is  2237
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=8
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.75
x_eval_time shape before reshape: (1609, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1889, 14)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (172, 20)
store_x_eval.shape: (169, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (203, 20)
Model: "sequential_85"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_171 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_76 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_172 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_77 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_84 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 61ms/step - loss: 35906.1133 - val_loss: 3098.5312
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 9692.7617 - val_loss: 552.1101
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1690.1912 - val_loss: 175.4864
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 766.7796 - val_loss: 447.1161
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1100.0265 - val_loss: 79.4871
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 407.0858 - val_loss: 47.7252
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 265.1601 - val_loss: 31.2024
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 184.3420 - val_loss: 22.5759
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 133.9280 - val_loss: 17.4127
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 102.7733 - val_loss: 15.1096
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 80.3628 - val_loss: 10.9220
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 66.7212 - val_loss: 9.1333
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 54.1863 - val_loss: 6.7583
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 40.6570 - val_loss: 5.4371
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 32.6830 - val_loss: 4.8515
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 27.5125 - val_loss: 3.7809
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 22.3841 - val_loss: 3.4011
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 19.6381 - val_loss: 3.1840
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 16.0307 - val_loss: 2.7746
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 13.7825 - val_loss: 2.4479
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 11.9829 - val_loss: 2.2525
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 10.3095 - val_loss: 2.0521
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 9.2209 - val_loss: 1.9278
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 8.3043 - val_loss: 1.6993
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 7.3547 - val_loss: 1.6626
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 6.6985 - val_loss: 1.8397
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 5.3176 - val_loss: 1.4785
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 5.9919 - val_loss: 1.2876
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 4.6347 - val_loss: 1.1037
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 4.1668 - val_loss: 0.9896
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 4.0469 - val_loss: 0.9301
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 3.4501 - val_loss: 0.6807
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 2.8242 - val_loss: 0.6639
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 2.7743 - val_loss: 0.5368
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 2.3835 - val_loss: 0.4270
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 2.0215 - val_loss: 0.3550
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1.8976 - val_loss: 0.2845
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1.7322 - val_loss: 0.3456
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1.6148 - val_loss: 0.2627
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1.4451 - val_loss: 0.2187
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1.1542 - val_loss: 0.1632
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1.1060 - val_loss: 0.2591
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1.1326 - val_loss: 0.1438
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.8585 - val_loss: 0.1240
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.7892 - val_loss: 0.1355
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.8256 - val_loss: 0.1008
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.6729 - val_loss: 0.1728
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.6883 - val_loss: 0.0846
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.6339 - val_loss: 0.0933
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.4695 - val_loss: 0.0824
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.5237 - val_loss: 0.1078
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.3803 - val_loss: 0.0703
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.4690 - val_loss: 0.0620
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.3413 - val_loss: 0.0518
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.3548 - val_loss: 0.2102
Epoch 56/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.3189 - val_loss: 0.1197
Epoch 57/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.3782 - val_loss: 0.0510
Epoch 58/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.2319 - val_loss: 0.0927
Epoch 59/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.2630 - val_loss: 0.0687
Epoch 60/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.2632 - val_loss: 0.0904
Epoch 61/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.2405 - val_loss: 0.0333
Epoch 62/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.2046 - val_loss: 0.0379
Epoch 63/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.1729 - val_loss: 0.1338
Epoch 64/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.2571 - val_loss: 0.0399
Epoch 65/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.1710 - val_loss: 0.0373
Epoch 66/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.3139 - val_loss: 0.0308
Epoch 67/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.1369 - val_loss: 0.0311
Epoch 68/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.1401 - val_loss: 0.0990
Epoch 69/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.5754 - val_loss: 0.1784
Epoch 70/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.5505 - val_loss: 0.1179
Epoch 71/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.2199 - val_loss: 0.0234
Epoch 72/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.1643 - val_loss: 0.3613
Epoch 73/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.3534 - val_loss: 0.0332
Epoch 74/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.1499 - val_loss: 0.0130
Epoch 75/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.2689 - val_loss: 0.0211
Epoch 76/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.2733 - val_loss: 0.3388
Epoch 77/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.4954 - val_loss: 0.2240
Epoch 78/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.3152 - val_loss: 0.1716
Epoch 79/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.3177 - val_loss: 0.0240
Epoch 80/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.4918 - val_loss: 0.2408
Epoch 81/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.4156 - val_loss: 0.0266
Epoch 82/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.2693 - val_loss: 0.0113
Epoch 83/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.0843 - val_loss: 0.2548
Epoch 84/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.1940 - val_loss: 0.0148
Epoch 85/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.1293 - val_loss: 0.3965
Epoch 86/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.2155 - val_loss: 0.3089
Epoch 87/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.8342 - val_loss: 0.0415
Epoch 88/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.1534 - val_loss: 0.4001
Epoch 89/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.2687 - val_loss: 0.0178
Epoch 90/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.1409 - val_loss: 0.3309
Epoch 91/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.2876 - val_loss: 0.0309
Epoch 92/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.1821 - val_loss: 0.3889
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step 
Sample raw predictions (after inverse transform and clipping): [0.35188085 0.0742069  0.04478479 0.27248406 0.169245  ]
RMSE =  0.8047374
Validation R-squared for item 2237: -0.7029622793197632
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=10.792951583862305
True y_val range (after inverse transform): min=0.0, max=6.0
No description has been provided for this image
-----------------------------------
Current item is  1016
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=22
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7272727272727273
x_eval_time shape before reshape: (1573, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1853, 14)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (164, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (161, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (181, 20)
Model: "sequential_86"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_173 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_78 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_174 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_79 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_85 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 63ms/step - loss: 8923.8477 - val_loss: 446.0733
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 585.8268 - val_loss: 41.1710
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 75.2053 - val_loss: 2.0860
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 5.8188 - val_loss: 0.0454
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 10.3395 - val_loss: 0.0849
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.9538 - val_loss: 0.0342
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.4927 - val_loss: 0.0214
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.1630 - val_loss: 0.1652
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.9384 - val_loss: 0.1279
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.9271 - val_loss: 0.0940
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.6017 - val_loss: 0.0327
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.6964 - val_loss: 0.0914
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.7704 - val_loss: 0.0204
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.3228 - val_loss: 0.0211
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.5552 - val_loss: 0.0409
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.4082 - val_loss: 0.0210
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.4093 - val_loss: 0.3394
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.2546 - val_loss: 0.0199
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.2221 - val_loss: 0.0410
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.1545 - val_loss: 0.0205
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 0.3061 - val_loss: 0.0684
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 0.2440 - val_loss: 1.0419
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.4100 - val_loss: 0.0257
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0456 - val_loss: 0.0936
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 0.0454 - val_loss: 0.0148
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0545 - val_loss: 0.0598
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.1556 - val_loss: 0.0149
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0604 - val_loss: 0.0213
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0713 - val_loss: 0.1428
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0981 - val_loss: 0.0273
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.1685 - val_loss: 0.1215
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0597 - val_loss: 0.0130
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0649 - val_loss: 0.0106
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0711 - val_loss: 0.0099
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0408 - val_loss: 0.0269
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0344 - val_loss: 0.0331
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.1083 - val_loss: 0.0118
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.1911 - val_loss: 0.0137
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.1547 - val_loss: 0.0140
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0544 - val_loss: 0.0161
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.1445 - val_loss: 0.0099
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.3114 - val_loss: 0.0095
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0554 - val_loss: 0.0253
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.2086 - val_loss: 0.0214
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 0.1929 - val_loss: 0.0183
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0879 - val_loss: 0.0641
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.3170 - val_loss: 0.0097
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0879 - val_loss: 0.0299
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0576 - val_loss: 0.0260
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0240 - val_loss: 0.0102
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0718 - val_loss: 0.0101
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.2780 - val_loss: 0.0094
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0189 - val_loss: 0.0092
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0185 - val_loss: 0.0101
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0286 - val_loss: 0.0102
Epoch 56/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0123 - val_loss: 0.0097
Epoch 57/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0196 - val_loss: 0.0102
Epoch 58/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0714 - val_loss: 0.8117
Epoch 59/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.1675 - val_loss: 0.0112
Epoch 60/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0785 - val_loss: 0.0106
Epoch 61/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0140 - val_loss: 0.0101
Epoch 62/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0377 - val_loss: 0.0098
Epoch 63/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 0.0159 - val_loss: 0.0096
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step 
Sample raw predictions (after inverse transform and clipping): [2.4353821 2.4648445 2.0017042 1.867104  0.9972872]
RMSE =  2.1586742
Validation R-squared for item 1016: -0.09603071212768555
50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=6.3863701820373535
True y_val range (after inverse transform): min=0.0, max=16.0
No description has been provided for this image
-----------------------------------
Current item is  2114
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6000000000000001
x_eval_time shape before reshape: (1659, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1939, 14)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (176, 20)
Model: "sequential_87"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_175 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_80 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_176 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_81 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_86 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 66ms/step - loss: 34148.1094 - val_loss: 5815.8286
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 4641.6841 - val_loss: 77.1193
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 160.8722 - val_loss: 44.5445
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 45.6440 - val_loss: 23.8772
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 24.9034 - val_loss: 6.7086
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 18.1216 - val_loss: 3.2390
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 11.7563 - val_loss: 1.0781
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 9.5555 - val_loss: 1.2092
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 8.1537 - val_loss: 1.2637
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 6.9849 - val_loss: 0.3593
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 5.9574 - val_loss: 2.4656
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 5.4623 - val_loss: 0.3310
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 4.2194 - val_loss: 1.7716
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 3.9904 - val_loss: 0.3303
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 2.8858 - val_loss: 0.1154
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 2.3539 - val_loss: 0.1850
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 2.2482 - val_loss: 0.2820
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.7259 - val_loss: 0.4737
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.6392 - val_loss: 0.0176
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.4088 - val_loss: 0.0515
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.2060 - val_loss: 0.0236
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.3442 - val_loss: 0.0751
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.2429 - val_loss: 0.3209
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.0368 - val_loss: 0.1124
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.9989 - val_loss: 0.0740
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.7074 - val_loss: 0.1335
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.6768 - val_loss: 0.7925
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 0.6492 - val_loss: 0.0512
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.6420 - val_loss: 0.1251
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step 
Sample raw predictions (after inverse transform and clipping): [0. 0. 0. 0. 0.]
RMSE =  0.6571891
Validation R-squared for item 2114: -0.9010403156280518
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=7.650656700134277
True y_val range (after inverse transform): min=0.0, max=6.0
No description has been provided for this image
-----------------------------------
Current item is  2236
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=5
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8
x_eval_time shape before reshape: (1626, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1906, 14)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (167, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (202, 20)
Model: "sequential_88"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_177 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_82 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_178 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_83 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_87 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 66ms/step - loss: 60931.7383 - val_loss: 18196.5117
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 54921.3125 - val_loss: 703.8808
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 2274.4390 - val_loss: 69.0283
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 551.3114 - val_loss: 27.0784
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 261.7172 - val_loss: 14.9578
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 147.7003 - val_loss: 3.5253
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 189.1150 - val_loss: 8.1673
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 248.2841 - val_loss: 13.5123
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 149.0077 - val_loss: 15.1317
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 83.7115 - val_loss: 6.9724
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 60.3978 - val_loss: 4.7333
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 49.8008 - val_loss: 5.4633
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 38.1581 - val_loss: 3.4983
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 35.3562 - val_loss: 8.0386
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 32.1800 - val_loss: 4.1421
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 28.0170 - val_loss: 2.5485
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 21.0005 - val_loss: 7.2916
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 20.1295 - val_loss: 2.2763
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 18.7297 - val_loss: 3.6458
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 15.1441 - val_loss: 7.6980
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 14.9756 - val_loss: 0.9691
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 11.4808 - val_loss: 0.7378
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 10.3961 - val_loss: 4.0869
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 10.9336 - val_loss: 2.5544
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 8.9745 - val_loss: 1.7377
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 7.3794 - val_loss: 6.7601
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 8.3014 - val_loss: 0.5705
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 6.2151 - val_loss: 0.5179
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 6.6011 - val_loss: 0.4941
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 6.6561 - val_loss: 2.3238
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 6.2074 - val_loss: 0.8638
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 6.9182 - val_loss: 2.3331
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 12.7313 - val_loss: 2.2567
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 11.0322 - val_loss: 0.7912
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 7.2435 - val_loss: 2.1257
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 6.4761 - val_loss: 0.9910
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 5.7495 - val_loss: 0.3297
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 4.9290 - val_loss: 0.3290
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 4.1679 - val_loss: 0.2693
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 3.9212 - val_loss: 2.8054
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 3.5207 - val_loss: 0.2506
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 3.5214 - val_loss: 2.3607
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 2.9373 - val_loss: 0.2157
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 2.4879 - val_loss: 0.1856
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 2.5557 - val_loss: 0.1589
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 2.3990 - val_loss: 0.2718
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 2.4032 - val_loss: 0.7873
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 2.2215 - val_loss: 0.6562
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 2.0798 - val_loss: 0.5002
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 1.7341 - val_loss: 0.3142
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 1.7402 - val_loss: 0.3478
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.7864 - val_loss: 0.5294
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 1.9512 - val_loss: 0.1546
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.7519 - val_loss: 0.0617
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.3610 - val_loss: 3.0020
Epoch 56/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 2.0613 - val_loss: 0.1477
Epoch 57/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.0915 - val_loss: 2.1934
Epoch 58/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.8796 - val_loss: 0.3350
Epoch 59/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.4961 - val_loss: 0.2938
Epoch 60/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.5036 - val_loss: 0.0486
Epoch 61/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 1.2643 - val_loss: 0.0517
Epoch 62/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 1.1046 - val_loss: 0.1592
Epoch 63/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.9828 - val_loss: 0.8942
Epoch 64/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.0303 - val_loss: 0.1532
Epoch 65/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.1882 - val_loss: 0.1869
Epoch 66/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.9287 - val_loss: 0.3617
Epoch 67/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.7628 - val_loss: 0.7922
Epoch 68/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.8046 - val_loss: 0.0245
Epoch 69/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.6668 - val_loss: 0.0250
Epoch 70/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.5698 - val_loss: 0.2881
Epoch 71/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.8082 - val_loss: 0.2803
Epoch 72/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.5431 - val_loss: 0.6373
Epoch 73/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.7606 - val_loss: 2.3125
Epoch 74/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.8540 - val_loss: 0.1715
Epoch 75/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.4041 - val_loss: 0.0158
Epoch 76/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.8022 - val_loss: 0.0193
Epoch 77/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.6962 - val_loss: 0.4107
Epoch 78/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.6775 - val_loss: 0.1024
Epoch 79/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.6764 - val_loss: 0.5605
Epoch 80/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.9005 - val_loss: 0.2788
Epoch 81/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.3058 - val_loss: 0.0120
Epoch 82/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.2849 - val_loss: 2.4434
Epoch 83/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.6922 - val_loss: 0.1395
Epoch 84/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.6178 - val_loss: 0.0793
Epoch 85/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.2732 - val_loss: 0.2161
Epoch 86/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.3751 - val_loss: 0.0786
Epoch 87/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.2321 - val_loss: 0.0579
Epoch 88/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.2737 - val_loss: 0.0227
Epoch 89/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.1167 - val_loss: 0.3834
Epoch 90/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.3210 - val_loss: 0.1243
Epoch 91/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.5968 - val_loss: 0.5817
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step 
Sample raw predictions (after inverse transform and clipping): [0.19208461 0.37189746 0.47617587 0.3737444  0.30523837]
RMSE =  0.52220756
Validation R-squared for item 2236: -0.2848362922668457
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.9125242233276367
True y_val range (after inverse transform): min=0.0, max=4.0
No description has been provided for this image
-----------------------------------
Current item is  1172
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=30
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7666666666666666
x_eval_time shape before reshape: (1627, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1907, 14)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (216, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (172, 20)
store_x_eval.shape: (184, 20)
Model: "sequential_89"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_179 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_84 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_180 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_85 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_88 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 67ms/step - loss: 28333.6367 - val_loss: 1906.9412
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 2945.6123 - val_loss: 20.3263
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 146.3445 - val_loss: 5.5245
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 54.6891 - val_loss: 2.7626
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 27.4813 - val_loss: 3.5591
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 17.9773 - val_loss: 0.3740
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 12.3851 - val_loss: 1.6670
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 9.3086 - val_loss: 9.4532
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 95.3638 - val_loss: 1.4107
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 59.0693 - val_loss: 0.5416
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 26.8820 - val_loss: 1.9346
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 17.2062 - val_loss: 0.3527
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 10.3359 - val_loss: 0.4483
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 8.6844 - val_loss: 0.6080
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 6.0948 - val_loss: 0.9279
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 5.5852 - val_loss: 2.1914
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 4.6008 - val_loss: 0.3030
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 3.9576 - val_loss: 0.3910
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 4.2066 - val_loss: 0.1964
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 2.4054 - val_loss: 0.1915
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 2.5420 - val_loss: 0.3455
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 2.1845 - val_loss: 0.5793
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 1.9163 - val_loss: 2.6166
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 1.9376 - val_loss: 0.1257
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 1.9324 - val_loss: 0.1128
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 1.7069 - val_loss: 0.0205
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 1.2848 - val_loss: 1.6870
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 1.6824 - val_loss: 0.0569
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.8168 - val_loss: 0.0331
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.9169 - val_loss: 0.0144
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.7331 - val_loss: 0.2180
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.7197 - val_loss: 3.8089
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.8007 - val_loss: 0.0406
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.5251 - val_loss: 0.6182
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.6502 - val_loss: 0.0078
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.3353 - val_loss: 0.5958
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.3470 - val_loss: 0.4124
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 5.9941 - val_loss: 0.0731
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 1.1937 - val_loss: 0.0029
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.5281 - val_loss: 0.0111
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.3368 - val_loss: 0.0018
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 1.0972 - val_loss: 0.7724
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 3.3587 - val_loss: 0.0584
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 1.3964 - val_loss: 0.0080
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.7911 - val_loss: 0.0120
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.3834 - val_loss: 0.0052
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.4767 - val_loss: 0.0197
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.4160 - val_loss: 0.0031
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.2573 - val_loss: 0.0342
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.2889 - val_loss: 0.0079
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1585 - val_loss: 0.0046
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step 
Sample raw predictions (after inverse transform and clipping): [0.48300707 0.         0.35337994 0.17798683 0.64058304]
RMSE =  1.4293249
Validation R-squared for item 1172: -0.2067563533782959
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=14.141376495361328
True y_val range (after inverse transform): min=0.0, max=23.0
No description has been provided for this image
-----------------------------------
Current item is  2443
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=8
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.25
x_eval_time shape before reshape: (1657, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1937, 14)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (223, 20)
store_x_eval.shape: (212, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (190, 20)
Model: "sequential_90"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_181 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_86 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_182 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_87 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_89 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 69ms/step - loss: 8864.8467 - val_loss: 278.3709
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 636.4620 - val_loss: 42.4217
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 148.2120 - val_loss: 7.5348
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 47.5936 - val_loss: 6.1026
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 33.8543 - val_loss: 2.5773
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 16.4389 - val_loss: 1.7642
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 17.4370 - val_loss: 3.6098
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 11.6453 - val_loss: 0.5502
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 2.2272 - val_loss: 0.1828
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 2.2771 - val_loss: 0.3212
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 2.8712 - val_loss: 0.1371
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 2.1052 - val_loss: 3.9842
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 3.2921 - val_loss: 0.1209
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 2.1808 - val_loss: 0.0278
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 1.2795 - val_loss: 0.0330
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.9899 - val_loss: 1.3131
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.8943 - val_loss: 0.0171
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.4222 - val_loss: 0.0055
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.3730 - val_loss: 0.0131
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1982 - val_loss: 0.0765
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.1458 - val_loss: 0.0087
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 2.0024 - val_loss: 0.0303
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1923 - val_loss: 0.0115
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.2658 - val_loss: 0.0121
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.3012 - val_loss: 0.4320
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 1.6817 - val_loss: 0.0127
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.2560 - val_loss: 0.0242
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0771 - val_loss: 0.0050
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1192 - val_loss: 0.0051
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1292 - val_loss: 0.0052
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0815 - val_loss: 0.0049
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1348 - val_loss: 0.0045
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0847 - val_loss: 0.0402
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0641 - val_loss: 0.0065
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0844 - val_loss: 0.0054
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1272 - val_loss: 0.0075
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0509 - val_loss: 0.0043
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0774 - val_loss: 0.3006
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1052 - val_loss: 0.0382
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0823 - val_loss: 0.0060
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0473 - val_loss: 0.0290
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1634 - val_loss: 0.0050
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0846 - val_loss: 0.0092
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1101 - val_loss: 0.0137
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0412 - val_loss: 0.0178
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1236 - val_loss: 0.0107
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0313 - val_loss: 0.0671
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step 
Sample raw predictions (after inverse transform and clipping): [0.30323613 0.46130294 0.4934683  0.3977915  0.35509613]
RMSE =  0.7468081
Validation R-squared for item 2443: -0.05250751972198486
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.19840669631958
True y_val range (after inverse transform): min=0.0, max=10.0
No description has been provided for this image
-----------------------------------
Current item is  1004
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=36
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7222222222222222
x_eval_time shape before reshape: (1680, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1960, 14)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (162, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (213, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (208, 20)
Model: "sequential_91"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_183 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_88 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_184 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_89 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_90 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 71ms/step - loss: 3144.2327 - val_loss: 33.5587
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 82.9533 - val_loss: 5.7705
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 22.6164 - val_loss: 2.0536
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 8.5708 - val_loss: 0.7341
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 4.7551 - val_loss: 0.9842
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 3.6357 - val_loss: 0.2936
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 2.1022 - val_loss: 0.2480
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 1.5662 - val_loss: 0.5586
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 1.0424 - val_loss: 0.0927
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.7866 - val_loss: 1.0064
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.7424 - val_loss: 0.0536
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.5426 - val_loss: 0.1050
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.4111 - val_loss: 0.0615
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.3940 - val_loss: 0.0203
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.2663 - val_loss: 0.0186
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.5111 - val_loss: 0.0221
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.1511 - val_loss: 0.0337
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.1710 - val_loss: 0.0821
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.2000 - val_loss: 0.0436
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.1767 - val_loss: 0.0554
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.1028 - val_loss: 0.1914
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.0864 - val_loss: 0.0320
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.1488 - val_loss: 0.0062
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.1510 - val_loss: 0.0055
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.0808 - val_loss: 0.0124
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.2283 - val_loss: 0.0063
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.0791 - val_loss: 0.0113
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.0820 - val_loss: 0.0389
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.0673 - val_loss: 0.0104
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 70ms/step - loss: 0.0887 - val_loss: 0.0917
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.1284 - val_loss: 0.0056
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.0390 - val_loss: 0.0421
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.0436 - val_loss: 0.0149
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.0349 - val_loss: 0.0290
50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step 
Sample raw predictions (after inverse transform and clipping): [0.66863996 0.04824355 1.6085396  4.9664354  0.7180111 ]
RMSE =  3.1583416
Validation R-squared for item 1004: -0.4444699287414551
53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=8.026754379272461
True y_val range (after inverse transform): min=0.0, max=23.0
No description has been provided for this image
-----------------------------------
Current item is  2297
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=8
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.625
x_eval_time shape before reshape: (1694, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1974, 14)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (213, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (220, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (199, 20)
Model: "sequential_92"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_185 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_90 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_186 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_91 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_91 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 70ms/step - loss: 20200.9199 - val_loss: 2367.4358
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 3315.8997 - val_loss: 40.7881
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 590.1129 - val_loss: 16.0045
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 220.4137 - val_loss: 8.5887
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 112.3401 - val_loss: 1.4584
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 59.8091 - val_loss: 4.6452
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 39.8194 - val_loss: 0.3325
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 25.9247 - val_loss: 0.4017
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 16.2369 - val_loss: 0.1149
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 13.7494 - val_loss: 4.0648
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 7.5500 - val_loss: 0.1244
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 5.0763 - val_loss: 2.2661
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 4.1615 - val_loss: 0.4402
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 3.1275 - val_loss: 0.1856
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 2.7987 - val_loss: 0.0710
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 2.2142 - val_loss: 0.6639
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 1.6944 - val_loss: 0.4843
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 1.8092 - val_loss: 1.1986
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 2.3310 - val_loss: 0.5549
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 2.2343 - val_loss: 0.1168
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 1.5023 - val_loss: 0.0206
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 1.3591 - val_loss: 1.4173
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 1.4182 - val_loss: 0.0239
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 1.5187 - val_loss: 0.0398
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.9765 - val_loss: 0.0293
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.9338 - val_loss: 1.0571
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 1.1464 - val_loss: 0.2968
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 1.6975 - val_loss: 0.0567
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.8730 - val_loss: 2.6570
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 1.4159 - val_loss: 0.4466
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.5926 - val_loss: 0.0691
49/49 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step 
Sample raw predictions (after inverse transform and clipping): [0.13341664 0.42512035 0.92939734 0.46399993 0.        ]
RMSE =  0.7633648
Validation R-squared for item 2297: -0.2760545015335083
53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.9584254026412964
True y_val range (after inverse transform): min=0.0, max=5.0
No description has been provided for this image
-----------------------------------
Current item is  1347
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=18
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.611111111111111
x_eval_time shape before reshape: (1690, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1970, 14)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (223, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (176, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (220, 20)
Model: "sequential_93"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_187 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_92 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_188 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_93 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_92 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 72ms/step - loss: 18057.6504 - val_loss: 2621.0117
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 1595.1162 - val_loss: 31.2051
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 74.5870 - val_loss: 6.8534
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 21.3470 - val_loss: 2.5522
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 9.9546 - val_loss: 1.1069
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 7.5987 - val_loss: 0.4591
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 6.4161 - val_loss: 0.5092
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 2.6037 - val_loss: 0.1205
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.7250 - val_loss: 0.0569
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.4639 - val_loss: 0.0170
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.2334 - val_loss: 0.0588
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.2327 - val_loss: 0.0282
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.1847 - val_loss: 0.0070
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0983 - val_loss: 0.0051
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.1256 - val_loss: 0.0101
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0799 - val_loss: 0.0058
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.2580 - val_loss: 0.0674
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.1226 - val_loss: 0.0706
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0950 - val_loss: 0.0037
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.1553 - val_loss: 0.0133
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 70ms/step - loss: 0.0826 - val_loss: 0.0036
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.1042 - val_loss: 0.0528
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.1396 - val_loss: 0.1476
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.1052 - val_loss: 0.0048
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0316 - val_loss: 0.0089
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.1060 - val_loss: 0.0084
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0801 - val_loss: 0.0427
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0989 - val_loss: 0.0033
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 70ms/step - loss: 0.0497 - val_loss: 0.0047
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0591 - val_loss: 0.0142
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0362 - val_loss: 0.0385
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0968 - val_loss: 0.0138
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0405 - val_loss: 0.0041
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0659 - val_loss: 0.0081
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 70ms/step - loss: 0.2455 - val_loss: 0.0044
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0284 - val_loss: 0.0079
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0323 - val_loss: 0.0068
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 70ms/step - loss: 8.1734 - val_loss: 0.0225
50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step 
Sample raw predictions (after inverse transform and clipping): [0.7583719  0.60148513 0.64859056 0.42187887 1.1037309 ]
RMSE =  1.9945282
Validation R-squared for item 1347: -0.27146196365356445
53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step
Predicted y_val range (after inverse transform and clipping): min=0.029141077771782875, max=2.4545814990997314
True y_val range (after inverse transform): min=0.0, max=11.0
No description has been provided for this image
-----------------------------------
Current item is  2507
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.0
x_eval_time shape before reshape: (1611, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1891, 14)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (169, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (199, 20)
Model: "sequential_94"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_189 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_94 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_190 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_95 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_93 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 74ms/step - loss: 5045.1255 - val_loss: 14.3661
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 48.3462 - val_loss: 6.0955
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 11.8565 - val_loss: 3.5488
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 5.1381 - val_loss: 1.7383
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 3.1490 - val_loss: 0.6675
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 2.0620 - val_loss: 0.3997
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 1.5252 - val_loss: 0.3390
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 1.0590 - val_loss: 0.1710
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.9370 - val_loss: 0.1428
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.6628 - val_loss: 0.0814
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.6023 - val_loss: 0.2840
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 71ms/step - loss: 0.4327 - val_loss: 0.0848
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.5873 - val_loss: 0.1352
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.7596 - val_loss: 0.0600
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.6082 - val_loss: 0.0937
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.4349 - val_loss: 0.0431
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.3078 - val_loss: 0.0396
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.2941 - val_loss: 0.0370
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.1955 - val_loss: 0.7451
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.2799 - val_loss: 0.0965
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.2239 - val_loss: 0.0182
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.1574 - val_loss: 0.0143
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.1303 - val_loss: 0.1382
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.2218 - val_loss: 0.0307
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.1162 - val_loss: 0.0127
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.2133 - val_loss: 0.0423
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.1700 - val_loss: 0.0118
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.1624 - val_loss: 0.0238
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.0908 - val_loss: 0.0330
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.0812 - val_loss: 0.0478
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.2059 - val_loss: 0.1485
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.0758 - val_loss: 0.0254
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.0974 - val_loss: 0.2549
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.1789 - val_loss: 0.0420
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.0380 - val_loss: 0.0258
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.0619 - val_loss: 0.0926
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 71ms/step - loss: 0.0377 - val_loss: 0.0638
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step 
Sample raw predictions (after inverse transform and clipping): [1.5021943  1.2365146  1.422947   1.4174192  0.38766748]
RMSE =  1.1200094
Validation R-squared for item 2507: -0.47574126720428467
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=9.151969909667969
True y_val range (after inverse transform): min=0.0, max=10.0
No description has been provided for this image
-----------------------------------
Current item is  1505
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=30
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.43333333333333335
x_eval_time shape before reshape: (1558, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1838, 14)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (166, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (173, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (173, 20)
Model: "sequential_95"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_191 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_96 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_192 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_97 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_94 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 9s 76ms/step - loss: 40563.8398 - val_loss: 8338.0137
Epoch 2/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 25712.9648 - val_loss: 878.3768
Epoch 3/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 1816.9257 - val_loss: 103.9630
Epoch 4/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 488.8260 - val_loss: 69.7238
Epoch 5/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 244.5220 - val_loss: 41.8512
Epoch 6/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 148.7149 - val_loss: 48.3145
Epoch 7/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 88.2482 - val_loss: 30.5061
Epoch 8/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 57.4573 - val_loss: 26.2248
Epoch 9/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 33.6125 - val_loss: 39.7281
Epoch 10/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 24.1754 - val_loss: 8.1977
Epoch 11/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 15.9730 - val_loss: 4.0010
Epoch 12/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 11.8706 - val_loss: 2.6758
Epoch 13/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 10.5190 - val_loss: 1.6969
Epoch 14/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 8.7530 - val_loss: 1.4315
Epoch 15/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 4.8835 - val_loss: 1.9010
Epoch 16/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 4.4317 - val_loss: 0.5931
Epoch 17/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 3.5682 - val_loss: 0.7071
Epoch 18/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 2.2628 - val_loss: 1.6902
Epoch 19/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 2.5115 - val_loss: 0.2347
Epoch 20/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 2.6315 - val_loss: 0.2980
Epoch 21/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 1.8519 - val_loss: 0.1893
Epoch 22/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 1.8228 - val_loss: 0.2928
Epoch 23/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 1.2240 - val_loss: 1.1044
Epoch 24/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 1.4955 - val_loss: 0.8322
Epoch 25/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 1.0327 - val_loss: 0.3763
Epoch 26/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 1.5035 - val_loss: 0.7492
Epoch 27/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.9371 - val_loss: 0.1841
Epoch 28/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.7479 - val_loss: 0.8157
Epoch 29/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.9058 - val_loss: 0.1164
Epoch 30/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.5682 - val_loss: 0.0855
Epoch 31/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.7420 - val_loss: 0.6139
Epoch 32/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.7602 - val_loss: 0.1204
Epoch 33/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.9145 - val_loss: 0.2997
Epoch 34/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.6201 - val_loss: 0.3324
Epoch 35/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.6609 - val_loss: 0.0393
Epoch 36/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.4978 - val_loss: 0.0764
Epoch 37/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.4933 - val_loss: 0.0991
Epoch 38/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.7066 - val_loss: 0.2675
Epoch 39/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.4802 - val_loss: 0.0114
Epoch 40/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.6731 - val_loss: 0.0578
Epoch 41/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.4568 - val_loss: 0.0417
Epoch 42/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.2156 - val_loss: 0.0888
Epoch 43/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.4979 - val_loss: 0.0352
Epoch 44/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.3809 - val_loss: 0.1698
Epoch 45/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.6103 - val_loss: 0.0890
Epoch 46/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.3648 - val_loss: 0.0226
Epoch 47/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 1.8748 - val_loss: 0.0444
Epoch 48/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.1101 - val_loss: 0.0486
Epoch 49/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.1617 - val_loss: 0.0361
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step 
Sample raw predictions (after inverse transform and clipping): [0.00346838 0.         0.         0.06643267 0.        ]
RMSE =  4.012579
Validation R-squared for item 1505: -2.548703193664551
49/49 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=108.49539184570312
True y_val range (after inverse transform): min=0.0, max=13.0
No description has been provided for this image
-----------------------------------
Current item is  1460
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=18
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7222222222222222
x_eval_time shape before reshape: (1594, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1874, 14)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (190, 20)
Model: "sequential_96"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_193 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_98 (Dropout)                 │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_194 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_99 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_95 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 74ms/step - loss: 11100.2441 - val_loss: 428.4152
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 638.3618 - val_loss: 97.8230
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 287.7589 - val_loss: 78.9735
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 187.6889 - val_loss: 18.8228
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 88.0253 - val_loss: 7.6022
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 33.7375 - val_loss: 3.5570
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 21.9986 - val_loss: 1.9975
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 15.2219 - val_loss: 1.5466
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 19.1399 - val_loss: 2.1772
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 17.2427 - val_loss: 1.1211
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 5.1295 - val_loss: 0.3648
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 1.3704 - val_loss: 0.3209
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 1.7105 - val_loss: 0.0259
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.2627 - val_loss: 0.0136
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.3877 - val_loss: 0.0518
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 0.2283 - val_loss: 0.6485
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.3794 - val_loss: 0.0124
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.3344 - val_loss: 0.2719
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.1388 - val_loss: 0.0412
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.1275 - val_loss: 0.3158
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.3066 - val_loss: 0.0159
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 0.2559 - val_loss: 0.1270
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.1239 - val_loss: 0.0246
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.4407 - val_loss: 0.0384
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.0708 - val_loss: 0.0534
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 0.2069 - val_loss: 0.1669
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.3986 - val_loss: 0.0417
53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step 
Sample raw predictions (after inverse transform and clipping): [0.        3.9262433 0.9943013 1.2408843 2.1202502]
RMSE =  1.7123482
Validation R-squared for item 1460: -0.7177166938781738
50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=7.560532093048096
True y_val range (after inverse transform): min=0.0, max=13.0
No description has been provided for this image
-----------------------------------
Current item is  2212
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=22
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8181818181818182
x_eval_time shape before reshape: (1653, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1933, 14)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (217, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (208, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (173, 20)
Model: "sequential_97"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_195 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_100 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_196 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_101 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_96 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 76ms/step - loss: 127880.2969 - val_loss: 52447.6719
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 145171.3750 - val_loss: 181.2726
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 3006.2612 - val_loss: 98.7306
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1033.6169 - val_loss: 33.7304
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 536.6387 - val_loss: 152.4569
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 387.2451 - val_loss: 57.1825
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 188.3111 - val_loss: 19.5302
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 114.4194 - val_loss: 41.5053
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 102.4929 - val_loss: 15.5725
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 85.3098 - val_loss: 9.8276
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 70.7391 - val_loss: 16.8651
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 67.4285 - val_loss: 11.0833
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 58.8396 - val_loss: 5.7798
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 51.6412 - val_loss: 22.2065
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 49.7030 - val_loss: 5.5185
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 41.4376 - val_loss: 4.5593
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 38.9224 - val_loss: 4.4720
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 33.8311 - val_loss: 14.6177
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 31.7345 - val_loss: 4.5170
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 29.5189 - val_loss: 4.6462
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 24.5128 - val_loss: 12.0110
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 27.2786 - val_loss: 9.7918
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 20.8509 - val_loss: 9.4445
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 19.9474 - val_loss: 1.8499
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 16.0964 - val_loss: 2.1507
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 14.9065 - val_loss: 4.0746
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 14.4174 - val_loss: 1.3643
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 13.4686 - val_loss: 1.2367
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 12.4834 - val_loss: 1.2074
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 10.4576 - val_loss: 4.8762
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 9.9904 - val_loss: 2.3661
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 9.0186 - val_loss: 0.8093
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 7.6027 - val_loss: 0.7528
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 7.0828 - val_loss: 1.9957
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 7.4795 - val_loss: 1.6094
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 6.2861 - val_loss: 1.4005
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 5.8326 - val_loss: 3.5256
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 5.8121 - val_loss: 2.7000
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 5.0468 - val_loss: 1.5516
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 4.8042 - val_loss: 0.6889
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 4.3103 - val_loss: 1.3074
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 4.2725 - val_loss: 0.7738
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 4.1835 - val_loss: 1.2574
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 3.8049 - val_loss: 0.7469
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 3.6386 - val_loss: 1.4427
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 3.5660 - val_loss: 3.2461
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 3.5348 - val_loss: 0.5815
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 2.9461 - val_loss: 1.2502
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 3.3271 - val_loss: 2.4640
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 2.9492 - val_loss: 4.2113
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 3.2329 - val_loss: 0.4322
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 2.2804 - val_loss: 4.5992
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 2.5633 - val_loss: 3.3557
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 2.5611 - val_loss: 0.9155
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 2.0922 - val_loss: 0.4254
Epoch 56/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 2.2412 - val_loss: 0.3239
Epoch 57/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.9286 - val_loss: 0.7634
Epoch 58/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 1.8097 - val_loss: 0.3344
Epoch 59/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.8003 - val_loss: 0.5530
Epoch 60/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 1.7467 - val_loss: 0.2500
Epoch 61/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.6733 - val_loss: 0.2223
Epoch 62/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.3543 - val_loss: 0.5486
Epoch 63/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.4484 - val_loss: 0.2925
Epoch 64/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.3129 - val_loss: 0.8146
Epoch 65/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.3410 - val_loss: 0.4445
Epoch 66/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 1.1855 - val_loss: 0.1476
Epoch 67/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.1799 - val_loss: 0.2163
Epoch 68/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 1.0644 - val_loss: 1.0367
Epoch 69/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.1678 - val_loss: 1.4934
Epoch 70/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.3753 - val_loss: 0.1226
Epoch 71/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.0703 - val_loss: 0.6932
Epoch 72/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.2698 - val_loss: 0.3904
Epoch 73/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 2.1696 - val_loss: 2.1317
Epoch 74/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 1.3632 - val_loss: 0.0397
Epoch 75/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.8072 - val_loss: 0.0745
Epoch 76/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.6892 - val_loss: 0.4290
Epoch 77/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.8815 - val_loss: 0.2997
Epoch 78/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.6377 - val_loss: 0.2541
Epoch 79/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.6004 - val_loss: 0.5579
Epoch 80/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.9896 - val_loss: 0.0685
Epoch 81/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.5595 - val_loss: 0.0480
Epoch 82/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.5252 - val_loss: 1.0152
Epoch 83/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.6530 - val_loss: 0.0359
Epoch 84/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.4277 - val_loss: 0.0379
Epoch 85/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.4247 - val_loss: 0.2772
Epoch 86/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.5318 - val_loss: 0.1598
Epoch 87/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 10.7463 - val_loss: 0.1932
Epoch 88/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 2.0304 - val_loss: 0.1506
Epoch 89/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.9633 - val_loss: 0.0303
Epoch 90/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 1.0629 - val_loss: 0.0303
Epoch 91/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.3049 - val_loss: 0.0549
Epoch 92/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.2868 - val_loss: 0.0311
Epoch 93/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.2720 - val_loss: 0.0297
Epoch 94/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.3549 - val_loss: 0.0241
Epoch 95/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.2720 - val_loss: 0.0225
Epoch 96/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.2358 - val_loss: 0.0248
Epoch 97/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.2260 - val_loss: 0.0290
Epoch 98/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.2389 - val_loss: 0.0304
Epoch 99/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.2231 - val_loss: 0.0248
Epoch 100/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.2077 - val_loss: 0.0230
53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step 
Sample raw predictions (after inverse transform and clipping): [3.9986901  0.         0.18433712 4.056968   0.        ]
RMSE =  2.2376428
Validation R-squared for item 2212: -0.9117189645767212
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=6.101165771484375
True y_val range (after inverse transform): min=0.0, max=18.0
No description has been provided for this image
-----------------------------------
Current item is  2467
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=9
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8888888888888888
x_eval_time shape before reshape: (1708, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1988, 14)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (209, 20)
store_x_eval.shape: (220, 20)
store_x_eval.shape: (213, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (182, 20)
Model: "sequential_98"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_197 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_102 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_198 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_103 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_97 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 77ms/step - loss: 9249.5352 - val_loss: 210.9825
Epoch 2/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 304.1246 - val_loss: 22.1171
Epoch 3/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 33.5302 - val_loss: 7.7710
Epoch 4/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 9.7997 - val_loss: 1.7165
Epoch 5/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 5.7006 - val_loss: 1.5319
Epoch 6/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 3.7727 - val_loss: 0.6032
Epoch 7/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 2.3605 - val_loss: 0.0444
Epoch 8/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 1.4063 - val_loss: 0.0256
Epoch 9/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.9961 - val_loss: 0.0190
Epoch 10/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.6394 - val_loss: 0.0180
Epoch 11/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.4089 - val_loss: 0.0300
Epoch 12/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.5264 - val_loss: 0.0105
Epoch 13/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.4333 - val_loss: 0.0246
Epoch 14/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.3833 - val_loss: 0.0092
Epoch 15/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.2669 - val_loss: 0.0114
Epoch 16/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.4744 - val_loss: 0.0786
Epoch 17/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.6441 - val_loss: 0.1019
Epoch 18/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.6838 - val_loss: 0.3421
Epoch 19/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.7510 - val_loss: 0.0827
Epoch 20/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.4266 - val_loss: 0.0212
Epoch 21/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.4069 - val_loss: 0.1917
Epoch 22/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.1947 - val_loss: 0.0168
Epoch 23/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.2018 - val_loss: 0.0269
Epoch 24/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.2975 - val_loss: 0.0175
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step 
Sample raw predictions (after inverse transform and clipping): [0.754617   0.8843739  0.75549024 0.76333183 1.0944864 ]
RMSE =  1.1438525
Validation R-squared for item 2467: -0.4422413110733032
54/54 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=15.418377876281738
True y_val range (after inverse transform): min=0.0, max=8.0
No description has been provided for this image
-----------------------------------
Current item is  1489
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=11
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6363636363636364
x_eval_time shape before reshape: (1581, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1861, 14)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (185, 20)
Model: "sequential_99"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_199 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_104 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_200 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_105 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_98 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 78ms/step - loss: 7672.4600 - val_loss: 53.0347
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 256.7303 - val_loss: 7.7590
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 66.4652 - val_loss: 5.0845
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 27.5519 - val_loss: 3.0844
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 15.1070 - val_loss: 1.3465
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 7.3645 - val_loss: 0.9973
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 5.9534 - val_loss: 0.2510
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 3.2752 - val_loss: 1.0692
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 2.2312 - val_loss: 0.5434
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 1.4472 - val_loss: 0.4504
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 1.3015 - val_loss: 0.2896
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.9176 - val_loss: 0.2507
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.6470 - val_loss: 0.1283
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.5782 - val_loss: 0.1067
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.4492 - val_loss: 0.0284
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 1.3571 - val_loss: 0.0300
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.6136 - val_loss: 0.2301
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.6118 - val_loss: 0.0653
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.4625 - val_loss: 0.1011
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.3544 - val_loss: 0.1276
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.2963 - val_loss: 0.1083
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.2818 - val_loss: 0.1005
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.2524 - val_loss: 0.0331
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.1958 - val_loss: 0.0368
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.1834 - val_loss: 0.0715
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step 
Sample raw predictions (after inverse transform and clipping): [1.4311068  1.4624445  1.3558273  0.10668732 1.0633548 ]
RMSE =  1.0941626
Validation R-squared for item 1489: -1.221785545349121
50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=3.040214776992798
True y_val range (after inverse transform): min=0.0, max=7.0
No description has been provided for this image
-----------------------------------
Current item is  2365
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=8
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5
x_eval_time shape before reshape: (1734, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (2014, 14)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (215, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (213, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (215, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (215, 20)
store_x_eval.shape: (183, 20)
Model: "sequential_100"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_201 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_106 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_202 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_107 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_99 (Dense)                     │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 12s 80ms/step - loss: 19351.8379 - val_loss: 717.8820
Epoch 2/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 2211.5840 - val_loss: 170.8561
Epoch 3/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 666.6846 - val_loss: 38.9263
Epoch 4/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 223.2035 - val_loss: 11.7611
Epoch 5/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 107.5310 - val_loss: 4.2626
Epoch 6/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 69.4284 - val_loss: 1.9447
Epoch 7/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 46.7131 - val_loss: 1.1654
Epoch 8/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 32.9381 - val_loss: 0.8632
Epoch 9/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 24.0009 - val_loss: 0.5804
Epoch 10/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 17.9426 - val_loss: 0.5417
Epoch 11/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 13.3386 - val_loss: 0.3123
Epoch 12/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 10.0706 - val_loss: 0.5559
Epoch 13/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 7.6576 - val_loss: 0.1100
Epoch 14/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 3.7758 - val_loss: 0.0703
Epoch 15/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.7915 - val_loss: 0.0356
Epoch 16/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.8454 - val_loss: 0.0230
Epoch 17/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.4594 - val_loss: 0.0078
Epoch 18/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.5934 - val_loss: 0.0529
Epoch 19/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.2378 - val_loss: 0.0829
Epoch 20/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.2745 - val_loss: 0.0147
Epoch 21/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.1707 - val_loss: 0.0116
Epoch 22/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.0994 - val_loss: 0.5190
Epoch 23/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.1954 - val_loss: 0.0225
Epoch 24/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.1341 - val_loss: 0.0091
Epoch 25/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.0714 - val_loss: 1.0447
Epoch 26/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.2614 - val_loss: 0.0297
Epoch 27/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.0841 - val_loss: 0.0204
53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step 
Sample raw predictions (after inverse transform and clipping): [0.5446452  0.70602715 0.04151163 0.49380937 0.055029  ]
RMSE =  0.48185068
Validation R-squared for item 2365: -2.038820266723633
55/55 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=3.197000741958618
True y_val range (after inverse transform): min=0.0, max=4.0
No description has been provided for this image
-----------------------------------
Current item is  2260
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=7
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7142857142857142
x_eval_time shape before reshape: (1716, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1996, 14)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (217, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (226, 20)
store_x_eval.shape: (212, 20)
Model: "sequential_101"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_203 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_108 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_204 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_109 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_100 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 10s 81ms/step - loss: 48576.5898 - val_loss: 3658.3506
Epoch 2/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 5587.9824 - val_loss: 62.7286
Epoch 3/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 950.8148 - val_loss: 29.5124
Epoch 4/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 377.9333 - val_loss: 25.8732
Epoch 5/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 250.7539 - val_loss: 17.6715
Epoch 6/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 230.8773 - val_loss: 19.0718
Epoch 7/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 171.1406 - val_loss: 17.2950
Epoch 8/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 124.1863 - val_loss: 13.1629
Epoch 9/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 92.2368 - val_loss: 31.1964
Epoch 10/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 67.4614 - val_loss: 11.5431
Epoch 11/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 54.3078 - val_loss: 14.7245
Epoch 12/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 43.0303 - val_loss: 5.1507
Epoch 13/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 33.7806 - val_loss: 6.2509
Epoch 14/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 26.6440 - val_loss: 3.8826
Epoch 15/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 22.1484 - val_loss: 3.2280
Epoch 16/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 19.3375 - val_loss: 1.7064
Epoch 17/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 15.0661 - val_loss: 2.8165
Epoch 18/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 11.4576 - val_loss: 1.9815
Epoch 19/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 10.6153 - val_loss: 1.2500
Epoch 20/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 9.0965 - val_loss: 1.4935
Epoch 21/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 8.0189 - val_loss: 0.9324
Epoch 22/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 6.8562 - val_loss: 0.8604
Epoch 23/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 9.0487 - val_loss: 1.1469
Epoch 24/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 7.3553 - val_loss: 1.1568
Epoch 25/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 6.6919 - val_loss: 0.4846
Epoch 26/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 5.1497 - val_loss: 1.1250
Epoch 27/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 5.5060 - val_loss: 1.7040
Epoch 28/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 4.0306 - val_loss: 0.2297
Epoch 29/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 4.1413 - val_loss: 1.5391
Epoch 30/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 3.5974 - val_loss: 0.5323
Epoch 31/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 3.3805 - val_loss: 1.4071
Epoch 32/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 3.3210 - val_loss: 0.4457
Epoch 33/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 2.9297 - val_loss: 0.3496
Epoch 34/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 2.6777 - val_loss: 0.3382
Epoch 35/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 1.9179 - val_loss: 0.7590
Epoch 36/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 1.8158 - val_loss: 0.2595
Epoch 37/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.8162 - val_loss: 0.0586
Epoch 38/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 2.1547 - val_loss: 0.0482
Epoch 39/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 2.1730 - val_loss: 0.5808
Epoch 40/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.6750 - val_loss: 0.0576
Epoch 41/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.9971 - val_loss: 0.3583
Epoch 42/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 2.2466 - val_loss: 0.0742
Epoch 43/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.9040 - val_loss: 0.0172
Epoch 44/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.8953 - val_loss: 0.2015
Epoch 45/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.8143 - val_loss: 0.1922
Epoch 46/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.7342 - val_loss: 0.3227
Epoch 47/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.7920 - val_loss: 0.9320
Epoch 48/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.6256 - val_loss: 0.0996
Epoch 49/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.4459 - val_loss: 0.0248
Epoch 50/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.6665 - val_loss: 0.0405
Epoch 51/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.4696 - val_loss: 0.0137
Epoch 52/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.5855 - val_loss: 0.0207
Epoch 53/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.5118 - val_loss: 0.8898
Epoch 54/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.5166 - val_loss: 0.0203
Epoch 55/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.6186 - val_loss: 0.0351
Epoch 56/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 1.2574 - val_loss: 0.0984
Epoch 57/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 1.1155 - val_loss: 0.1594
Epoch 58/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.7621 - val_loss: 0.3552
Epoch 59/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.4781 - val_loss: 0.0949
Epoch 60/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.7398 - val_loss: 0.2511
Epoch 61/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.6626 - val_loss: 0.0162
53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step 
Sample raw predictions (after inverse transform and clipping): [0.95859814 0.575128   0.61649907 0.3565337  0.6526011 ]
RMSE =  0.7649964
Validation R-squared for item 2260: -1.2166862487792969
54/54 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=2.758286476135254
True y_val range (after inverse transform): min=0.0, max=5.0
No description has been provided for this image
-----------------------------------
Current item is  1518
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=37
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.2702702702702703
x_eval_time shape before reshape: (1619, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1899, 14)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (209, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (168, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (174, 20)
Model: "sequential_102"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_205 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_110 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_206 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_111 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_101 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 80ms/step - loss: 28168.2852 - val_loss: 254.2339
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 2636.2229 - val_loss: 102.1992
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 588.9793 - val_loss: 58.8910
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 303.7220 - val_loss: 11.9821
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 172.9925 - val_loss: 3.5657
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 128.0503 - val_loss: 2.3995
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 70.0043 - val_loss: 2.1790
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 28.6356 - val_loss: 0.6924
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 17.1386 - val_loss: 0.8472
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 80ms/step - loss: 14.5312 - val_loss: 0.5145
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 12.5162 - val_loss: 0.4852
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 12.3351 - val_loss: 0.2900
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 6.9127 - val_loss: 0.2304
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 6.8316 - val_loss: 0.2942
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 6.5038 - val_loss: 0.2542
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 7.6964 - val_loss: 0.2580
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 5.2537 - val_loss: 0.1344
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 4.6520 - val_loss: 0.2165
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 3.6085 - val_loss: 0.0819
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 4.6730 - val_loss: 0.0517
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 3.0118 - val_loss: 0.0937
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 4.0927 - val_loss: 0.0338
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 2.4578 - val_loss: 0.0096
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 3.6347 - val_loss: 0.0097
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 4.1039 - val_loss: 0.3120
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.0016 - val_loss: 0.0112
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 1.4586 - val_loss: 0.0983
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.9907 - val_loss: 0.0068
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 1.7559 - val_loss: 0.0067
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.8311 - val_loss: 0.1449
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.2146 - val_loss: 0.0355
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.1079 - val_loss: 0.0103
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.5400 - val_loss: 0.1072
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.9629 - val_loss: 0.0255
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.9279 - val_loss: 0.0561
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.4441 - val_loss: 0.0470
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.6798 - val_loss: 0.0291
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.2575 - val_loss: 0.0031
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.5920 - val_loss: 0.0090
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.3342 - val_loss: 0.0536
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.3200 - val_loss: 0.0063
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.2110 - val_loss: 0.0012
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.2297 - val_loss: 0.0053
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.2453 - val_loss: 0.0014
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.1361 - val_loss: 0.0016
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.1401 - val_loss: 0.0113
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.2811 - val_loss: 0.0028
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.0908 - val_loss: 0.0301
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.5022 - val_loss: 0.0513
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.6103 - val_loss: 0.0035
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.0915 - val_loss: 0.0204
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.2164 - val_loss: 0.0054
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step 
Sample raw predictions (after inverse transform and clipping): [0.13276288 0.         0.12255774 0.         0.        ]
RMSE =  1.11512
Validation R-squared for item 1518: 0.03085637092590332
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.5972182750701904
True y_val range (after inverse transform): min=0.0, max=10.0
No description has been provided for this image
-----------------------------------
Current item is  2215
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7000000000000001
x_eval_time shape before reshape: (1638, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1918, 14)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (208, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (188, 20)
Model: "sequential_103"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_207 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_112 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_208 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_113 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_102 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 82ms/step - loss: 4645.4214 - val_loss: 13.0466
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 120.0400 - val_loss: 4.4863
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 26.9371 - val_loss: 1.7896
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 13.4880 - val_loss: 2.5927
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 7.6164 - val_loss: 0.1976
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 6.4936 - val_loss: 1.2857
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 5.3539 - val_loss: 0.1656
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 3.1342 - val_loss: 0.7137
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 3.0016 - val_loss: 0.0844
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 2.3135 - val_loss: 0.0360
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 1.2640 - val_loss: 0.0466
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 1.1337 - val_loss: 0.0512
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.7733 - val_loss: 0.0887
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.6675 - val_loss: 0.0574
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.5773 - val_loss: 0.0731
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.5111 - val_loss: 0.3907
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.8922 - val_loss: 0.0393
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.4145 - val_loss: 0.0335
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.5934 - val_loss: 0.0426
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.4455 - val_loss: 0.0183
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.3629 - val_loss: 0.0583
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.3091 - val_loss: 0.0174
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.2839 - val_loss: 0.6620
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.3292 - val_loss: 0.1936
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.2593 - val_loss: 0.0088
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.3407 - val_loss: 0.4215
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.4606 - val_loss: 0.0105
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1931 - val_loss: 0.0202
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.2361 - val_loss: 0.0669
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1782 - val_loss: 0.0084
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1254 - val_loss: 0.5630
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.1851 - val_loss: 0.0078
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.1780 - val_loss: 0.2528
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1525 - val_loss: 0.0199
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.2010 - val_loss: 0.0047
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.3022 - val_loss: 0.0254
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.1606 - val_loss: 0.0070
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1816 - val_loss: 0.0175
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1542 - val_loss: 0.0153
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1658 - val_loss: 0.0306
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1324 - val_loss: 0.0471
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1229 - val_loss: 0.0141
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.3362 - val_loss: 1.3314
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 1.1294 - val_loss: 0.0103
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.0422 - val_loss: 0.0235
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step 
Sample raw predictions (after inverse transform and clipping): [0.38735405 0.34696692 0.3587319  0.18122476 0.1310397 ]
RMSE =  0.77052706
Validation R-squared for item 2215: -0.058983445167541504
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=2.1956982612609863
True y_val range (after inverse transform): min=0.0, max=7.0
No description has been provided for this image
-----------------------------------
Current item is  2302
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=9
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7777777777777777
x_eval_time shape before reshape: (1595, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1875, 14)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (210, 20)
store_x_eval.shape: (176, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (175, 20)
Model: "sequential_104"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_209 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_114 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_210 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_115 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_103 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 80ms/step - loss: 36783.6797 - val_loss: 21775.6113
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 48349.5000 - val_loss: 806.7084
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 2414.4502 - val_loss: 219.2616
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 768.2894 - val_loss: 94.6791
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 332.0251 - val_loss: 16.7730
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 112.8234 - val_loss: 15.0475
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 62.1705 - val_loss: 8.2675
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 44.2032 - val_loss: 2.9688
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 23.0801 - val_loss: 1.5646
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 14.9223 - val_loss: 1.0083
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 7.1313 - val_loss: 0.4796
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 9.2141 - val_loss: 0.2242
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 6.1858 - val_loss: 0.1890
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 5.0102 - val_loss: 0.1498
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 3.3985 - val_loss: 0.1708
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 3.5042 - val_loss: 0.2163
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 2.7735 - val_loss: 0.2468
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.6163 - val_loss: 0.1178
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.7499 - val_loss: 0.0728
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.8258 - val_loss: 0.0369
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.4502 - val_loss: 0.0758
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 2.3290 - val_loss: 0.0480
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.4094 - val_loss: 0.0591
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.7964 - val_loss: 0.1051
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.5316 - val_loss: 0.0432
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.0390 - val_loss: 0.0617
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.8185 - val_loss: 0.0426
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.5901 - val_loss: 0.0705
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.5312 - val_loss: 0.1554
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.4164 - val_loss: 0.0520
53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step 
Sample raw predictions (after inverse transform and clipping): [0.73134065 0.         0.37978408 0.17293084 0.        ]
RMSE =  0.89458644
Validation R-squared for item 2302: -1.0573971271514893
50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=7.224201679229736
True y_val range (after inverse transform): min=0.0, max=7.0
No description has been provided for this image
-----------------------------------
Current item is  2374
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7000000000000001
x_eval_time shape before reshape: (1625, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1905, 14)
store_x_eval.shape: (167, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (166, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (181, 20)
Model: "sequential_105"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_211 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_116 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_212 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_117 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_104 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 82ms/step - loss: 26126.0391 - val_loss: 762.1906
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 1899.4292 - val_loss: 357.8877
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 815.6359 - val_loss: 111.7471
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 334.9939 - val_loss: 51.3484
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 214.7848 - val_loss: 19.6026
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 123.9470 - val_loss: 15.4822
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 73.9559 - val_loss: 7.8372
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 54.0721 - val_loss: 2.5749
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 23.2246 - val_loss: 1.2953
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 14.5510 - val_loss: 0.9505
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 8.0738 - val_loss: 0.6394
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 6.9885 - val_loss: 0.3841
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 4.7139 - val_loss: 0.2458
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 2.9241 - val_loss: 9.8497
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 3.2612 - val_loss: 0.1631
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 1.5156 - val_loss: 0.1123
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 1.3735 - val_loss: 0.0630
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1.0836 - val_loss: 0.0407
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 0.8269 - val_loss: 1.1462
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.8654 - val_loss: 0.0308
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.6427 - val_loss: 0.0430
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.5152 - val_loss: 1.4687
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.6018 - val_loss: 0.2635
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.5180 - val_loss: 0.0718
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.3381 - val_loss: 0.1419
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.3137 - val_loss: 0.1214
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.3266 - val_loss: 0.0177
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.3164 - val_loss: 0.0667
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.2888 - val_loss: 0.1725
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.3159 - val_loss: 0.0157
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.2401 - val_loss: 0.0223
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.3065 - val_loss: 0.4401
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.2827 - val_loss: 0.0166
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1989 - val_loss: 0.1114
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.3168 - val_loss: 0.0178
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1512 - val_loss: 0.0469
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1595 - val_loss: 0.0328
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1825 - val_loss: 0.0113
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1451 - val_loss: 0.0215
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1398 - val_loss: 0.1041
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.2884 - val_loss: 0.0163
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1341 - val_loss: 0.0103
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1502 - val_loss: 0.0092
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.2465 - val_loss: 0.0382
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.0996 - val_loss: 0.0262
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.0987 - val_loss: 0.0482
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.0667 - val_loss: 0.0092
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.0839 - val_loss: 0.0180
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.1824 - val_loss: 0.0311
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.0866 - val_loss: 0.0653
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.0862 - val_loss: 0.0094
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1345 - val_loss: 0.1842
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.0782 - val_loss: 0.0674
50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step 
Sample raw predictions (after inverse transform and clipping): [0.18363729 0.8050488  0.5737694  1.1469452  1.8379602 ]
RMSE =  1.0374782
Validation R-squared for item 2374: -0.2908381223678589
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=2.136575222015381
True y_val range (after inverse transform): min=0.0, max=7.0
No description has been provided for this image
-----------------------------------
Current item is  1251
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=26
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.46153846153846156
x_eval_time shape before reshape: (1686, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1966, 14)
store_x_eval.shape: (216, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (209, 20)
Model: "sequential_106"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_213 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_118 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_214 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_119 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_105 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 84ms/step - loss: 198958.6406 - val_loss: 24660.7441
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 65067.7812 - val_loss: 493.2159
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 6829.2407 - val_loss: 157.7820
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1772.8585 - val_loss: 223.5141
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1119.9945 - val_loss: 299.5569
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 685.0761 - val_loss: 98.6058
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 488.7709 - val_loss: 190.4696
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 294.9742 - val_loss: 40.8018
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 249.2172 - val_loss: 28.5109
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 172.5585 - val_loss: 12.7425
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 145.8395 - val_loss: 13.6653
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 111.0307 - val_loss: 8.4974
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 96.5358 - val_loss: 11.9968
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 83.7300 - val_loss: 17.2608
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 73.4851 - val_loss: 75.0582
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 75.6705 - val_loss: 5.8544
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 61.7437 - val_loss: 7.7614
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 55.6963 - val_loss: 5.7257
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 55.5335 - val_loss: 5.9441
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 44.9989 - val_loss: 15.1849
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 40.8252 - val_loss: 11.2726
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 37.0253 - val_loss: 4.9604
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 34.0081 - val_loss: 4.5218
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 34.8975 - val_loss: 3.2500
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 29.1669 - val_loss: 27.3662
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 29.2797 - val_loss: 2.8317
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 25.5027 - val_loss: 6.8082
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 24.7392 - val_loss: 2.9100
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 26.2334 - val_loss: 10.4113
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 22.5763 - val_loss: 22.8369
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 26.3348 - val_loss: 16.1530
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 20.9411 - val_loss: 2.1998
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 17.5564 - val_loss: 2.1519
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 102.6731 - val_loss: 3.0032
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 17.0103 - val_loss: 1.9555
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 17.5342 - val_loss: 11.5386
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 16.9134 - val_loss: 1.7581
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 12.9218 - val_loss: 1.7507
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 12.5564 - val_loss: 4.4047
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 13.1820 - val_loss: 1.4098
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 9.1679 - val_loss: 1.2403
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 10.7320 - val_loss: 10.6720
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 11.8756 - val_loss: 2.8240
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 7.7198 - val_loss: 10.8601
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 7.3677 - val_loss: 0.9620
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 8.0571 - val_loss: 12.1671
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 7.4508 - val_loss: 2.2071
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 5.9562 - val_loss: 1.8222
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 6.4859 - val_loss: 7.1637
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 7.0989 - val_loss: 1.3053
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 6.2648 - val_loss: 3.9454
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 4.8373 - val_loss: 0.5605
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 5.7271 - val_loss: 0.3936
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 3.8442 - val_loss: 4.0144
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 4.3560 - val_loss: 3.7193
Epoch 56/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 5.0977 - val_loss: 27.8877
Epoch 57/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 8.9851 - val_loss: 1.6578
Epoch 58/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 3.9492 - val_loss: 3.7848
Epoch 59/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 4.6180 - val_loss: 1.5677
Epoch 60/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 4.4640 - val_loss: 0.2999
Epoch 61/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 3.4251 - val_loss: 0.7334
Epoch 62/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 2.4335 - val_loss: 8.3348
Epoch 63/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 4.7043 - val_loss: 7.9175
Epoch 64/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 2.8513 - val_loss: 5.3727
Epoch 65/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 4.0095 - val_loss: 3.1687
Epoch 66/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 2.6600 - val_loss: 0.3541
Epoch 67/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 2.7761 - val_loss: 12.9142
Epoch 68/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 5.3904 - val_loss: 0.2808
Epoch 69/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 3.1049 - val_loss: 1.4748
Epoch 70/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1.7022 - val_loss: 6.5131
Epoch 71/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 4.4127 - val_loss: 0.9225
Epoch 72/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1.5083 - val_loss: 2.8427
Epoch 73/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 2.9067 - val_loss: 5.1120
Epoch 74/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 2.5581 - val_loss: 15.6357
Epoch 75/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 5.7102 - val_loss: 0.0899
Epoch 76/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1.8448 - val_loss: 0.4580
Epoch 77/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1.7421 - val_loss: 13.9468
Epoch 78/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 4.3565 - val_loss: 0.7292
Epoch 79/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 2.0403 - val_loss: 2.2832
Epoch 80/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 5.6222 - val_loss: 3.4456
Epoch 81/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1.4468 - val_loss: 0.4202
Epoch 82/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1.2842 - val_loss: 3.4947
Epoch 83/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 2.1503 - val_loss: 0.5687
Epoch 84/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1.4911 - val_loss: 0.5599
Epoch 85/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1.1094 - val_loss: 3.6159
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step 
Sample raw predictions (after inverse transform and clipping): [3.2665732 0.        0.        7.0570183 0.       ]
RMSE =  4.3291354
Validation R-squared for item 1251: -3.7527356147766113
53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=15.979684829711914
True y_val range (after inverse transform): min=0.0, max=12.0
No description has been provided for this image
-----------------------------------
Current item is  2163
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=12
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6666666666666666
x_eval_time shape before reshape: (1637, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1917, 14)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (173, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (198, 20)
Model: "sequential_107"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_215 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_120 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_216 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_121 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_106 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 84ms/step - loss: 11166.2637 - val_loss: 162.7639
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 476.2951 - val_loss: 3.2475
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 35.8252 - val_loss: 0.6841
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 10.9624 - val_loss: 0.5782
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 6.3534 - val_loss: 7.2067
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 5.5963 - val_loss: 0.0726
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 2.5653 - val_loss: 0.2542
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 1.6061 - val_loss: 0.4738
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 1.1392 - val_loss: 0.0192
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 1.2895 - val_loss: 8.5263
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 2.1536 - val_loss: 0.0060
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.4467 - val_loss: 0.2350
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.3328 - val_loss: 0.2186
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 86ms/step - loss: 0.3399 - val_loss: 0.1296
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.3041 - val_loss: 0.0165
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.4876 - val_loss: 0.0288
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.3039 - val_loss: 0.8052
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.4339 - val_loss: 0.5100
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.3816 - val_loss: 1.8579
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.5881 - val_loss: 0.1072
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.4374 - val_loss: 0.0089
54/54 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step 
Sample raw predictions (after inverse transform and clipping): [0. 0. 0. 0. 0.]
RMSE =  0.73363996
Validation R-squared for item 2163: -0.18651211261749268
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=2.7877354621887207
True y_val range (after inverse transform): min=0.0, max=8.0
No description has been provided for this image
-----------------------------------
Current item is  2482
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.4
x_eval_time shape before reshape: (1609, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1889, 14)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (197, 20)
Model: "sequential_108"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_217 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_122 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_218 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_123 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_107 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 84ms/step - loss: 62949.2891 - val_loss: 15878.2295
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 12563.6553 - val_loss: 955.4444
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 3039.5222 - val_loss: 411.9238
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1269.3661 - val_loss: 114.8837
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 569.7228 - val_loss: 82.5057
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 820.9339 - val_loss: 57.4599
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 336.9856 - val_loss: 26.7220
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 230.5864 - val_loss: 18.8633
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 151.3792 - val_loss: 11.6601
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 106.7263 - val_loss: 8.1631
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 87.6728 - val_loss: 6.5379
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 75.8701 - val_loss: 6.1110
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 55.3970 - val_loss: 5.9275
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 43.2164 - val_loss: 3.8283
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 35.4816 - val_loss: 2.4329
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 31.2489 - val_loss: 2.0393
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 23.4499 - val_loss: 1.6365
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 21.6298 - val_loss: 1.8035
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 18.1926 - val_loss: 1.2985
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 15.8595 - val_loss: 0.9764
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 14.3360 - val_loss: 2.1665
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 13.5899 - val_loss: 0.9781
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 11.6351 - val_loss: 0.8647
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 10.3888 - val_loss: 1.0711
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 85ms/step - loss: 9.5701 - val_loss: 0.6870
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 10.2550 - val_loss: 0.7303
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 9.1173 - val_loss: 0.4356
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 8.6127 - val_loss: 0.6266
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 7.8844 - val_loss: 2.1800
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 6.8211 - val_loss: 0.8884
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 6.9258 - val_loss: 0.5623
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 5.4083 - val_loss: 0.2992
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 4.9774 - val_loss: 0.6483
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 4.9454 - val_loss: 0.4409
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 3.8183 - val_loss: 0.4192
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 4.4337 - val_loss: 0.2810
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 3.8512 - val_loss: 0.6076
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 3.2678 - val_loss: 1.5353
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 3.1630 - val_loss: 0.3480
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 2.7755 - val_loss: 0.4863
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 2.4300 - val_loss: 0.3660
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 3.0885 - val_loss: 0.5167
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 2.3190 - val_loss: 0.3570
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 2.1299 - val_loss: 0.6472
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 1.8296 - val_loss: 0.6081
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 1.8675 - val_loss: 0.2893
53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step 
Sample raw predictions (after inverse transform and clipping): [0.        4.4759107 3.6678462 0.        0.       ]
RMSE =  3.1656024
Validation R-squared for item 2482: -41.6834602355957
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=14.068412780761719
True y_val range (after inverse transform): min=0.0, max=4.0
No description has been provided for this image
-----------------------------------
Current item is  1119
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=17
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.0588235294117647
x_eval_time shape before reshape: (1705, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1985, 14)
store_x_eval.shape: (227, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (229, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (190, 20)
Model: "sequential_109"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_219 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_124 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_220 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_125 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_108 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 10s 85ms/step - loss: 4667.2827 - val_loss: 40.3608
Epoch 2/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 149.6156 - val_loss: 3.3145
Epoch 3/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 18.2869 - val_loss: 0.7487
Epoch 4/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 4.5521 - val_loss: 0.0388
Epoch 5/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.6265 - val_loss: 0.0259
Epoch 6/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.3078 - val_loss: 0.0178
Epoch 7/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.4413 - val_loss: 0.0161
Epoch 8/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.1308 - val_loss: 0.0194
Epoch 9/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.2206 - val_loss: 0.0233
Epoch 10/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 85ms/step - loss: 0.0847 - val_loss: 0.0203
Epoch 11/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.0813 - val_loss: 0.1220
Epoch 12/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0918 - val_loss: 0.0263
Epoch 13/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.0682 - val_loss: 0.0426
Epoch 14/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0500 - val_loss: 0.0148
Epoch 15/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0452 - val_loss: 0.0243
Epoch 16/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.0580 - val_loss: 0.0941
Epoch 17/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.0386 - val_loss: 0.0148
Epoch 18/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0373 - val_loss: 0.1040
Epoch 19/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.1083 - val_loss: 0.0274
Epoch 20/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0357 - val_loss: 1.0668
Epoch 21/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.1984 - val_loss: 0.0194
Epoch 22/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.0299 - val_loss: 0.0352
Epoch 23/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.2334 - val_loss: 0.0148
Epoch 24/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0561 - val_loss: 0.0187
Epoch 25/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.0395 - val_loss: 0.0152
Epoch 26/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0178 - val_loss: 0.0144
Epoch 27/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0250 - val_loss: 0.0151
Epoch 28/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0561 - val_loss: 0.0201
Epoch 29/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0336 - val_loss: 0.0183
Epoch 30/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0371 - val_loss: 0.0185
Epoch 31/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0141 - val_loss: 0.0162
Epoch 32/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.1812 - val_loss: 0.0179
Epoch 33/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0182 - val_loss: 0.0255
Epoch 34/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.0311 - val_loss: 0.0181
Epoch 35/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0360 - val_loss: 0.0892
Epoch 36/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.0321 - val_loss: 0.0158
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step 
Sample raw predictions (after inverse transform and clipping): [1.6317894 1.7522495 1.4531772 1.7346601 1.4845957]
RMSE =  1.8835926
Validation R-squared for item 1119: -0.05382728576660156
54/54 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=3.1098947525024414
True y_val range (after inverse transform): min=0.0, max=18.0
No description has been provided for this image
-----------------------------------
Current item is  2190
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=30
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.43333333333333335
x_eval_time shape before reshape: (1632, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1912, 14)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (192, 20)
Model: "sequential_110"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_221 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_126 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_222 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_127 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_109 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 88ms/step - loss: 39265.3594 - val_loss: 1015.3320
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 3172.5127 - val_loss: 272.7464
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 666.9436 - val_loss: 159.3783
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 86ms/step - loss: 349.3012 - val_loss: 114.4419
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 157.0905 - val_loss: 5.3141
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 39.1010 - val_loss: 3.2479
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 22.3864 - val_loss: 1.4006
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 27.1769 - val_loss: 59.0822
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 86.9032 - val_loss: 12.1451
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 69.7718 - val_loss: 14.0733
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 26.5413 - val_loss: 2.6114
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 10.7584 - val_loss: 0.8082
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 6.9248 - val_loss: 0.6288
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 3.9365 - val_loss: 0.2698
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 2.5200 - val_loss: 0.4146
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 2.1708 - val_loss: 0.2064
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 1.2690 - val_loss: 1.6892
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 1.1132 - val_loss: 0.0732
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.7962 - val_loss: 0.1622
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.8259 - val_loss: 0.0353
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.6548 - val_loss: 0.1253
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.4723 - val_loss: 0.3059
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.3959 - val_loss: 0.4279
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.4237 - val_loss: 0.1511
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.4322 - val_loss: 1.3108
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.4245 - val_loss: 0.0247
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.3420 - val_loss: 0.0377
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.2814 - val_loss: 0.1904
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.4447 - val_loss: 0.0422
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.3095 - val_loss: 1.4548
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 1.1277 - val_loss: 0.0027
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.1554 - val_loss: 0.0404
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.4064 - val_loss: 0.0398
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.6038 - val_loss: 0.0213
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.2780 - val_loss: 0.0565
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 6.2050 - val_loss: 0.0227
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.1937 - val_loss: 0.0182
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.1173 - val_loss: 0.0216
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.2156 - val_loss: 0.0158
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.1013 - val_loss: 0.0045
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.1007 - val_loss: 0.0022
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.0491 - val_loss: 0.0079
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.0304 - val_loss: 0.0021
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.0278 - val_loss: 0.0059
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.0363 - val_loss: 0.0018
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.0401 - val_loss: 0.0684
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.1111 - val_loss: 0.0091
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.0396 - val_loss: 0.2533
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.0659 - val_loss: 1.6396
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.4599 - val_loss: 0.0049
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.1956 - val_loss: 0.0377
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.0880 - val_loss: 0.0793
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.0871 - val_loss: 0.0015
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.0340 - val_loss: 0.2282
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.4689 - val_loss: 0.0542
Epoch 56/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.0911 - val_loss: 9.1127e-04
Epoch 57/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.0152 - val_loss: 0.0174
Epoch 58/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.1222 - val_loss: 0.0019
Epoch 59/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.0921 - val_loss: 0.0071
Epoch 60/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.4162 - val_loss: 0.0011
Epoch 61/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.0357 - val_loss: 0.0682
Epoch 62/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.0609 - val_loss: 0.0950
Epoch 63/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.0602 - val_loss: 0.0353
Epoch 64/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.1805 - val_loss: 0.1106
Epoch 65/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.1051 - val_loss: 0.0043
Epoch 66/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.1306 - val_loss: 0.0114
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 25ms/step 
Sample raw predictions (after inverse transform and clipping): [0.83503187 0.54945946 0.92360103 0.73239404 0.43584126]
RMSE =  0.9302448
Validation R-squared for item 2190: -0.11616659164428711
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.4678949117660522
True y_val range (after inverse transform): min=0.0, max=13.0
No description has been provided for this image
-----------------------------------
Current item is  1071
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=24
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.625
x_eval_time shape before reshape: (1540, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1820, 14)
store_x_eval.shape: (165, 20)
store_x_eval.shape: (176, 20)
store_x_eval.shape: (172, 20)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (165, 20)
Model: "sequential_111"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_223 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_128 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_224 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_129 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_110 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 90ms/step - loss: 33086.4414 - val_loss: 108399.0234
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 82340.9531 - val_loss: 1208.8224
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 3363.2764 - val_loss: 164.1846
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 643.6526 - val_loss: 15.8108
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 43.5827 - val_loss: 6.1139
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 8.8013 - val_loss: 8.4074
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 6.8056 - val_loss: 2.9637
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 3.8029 - val_loss: 0.8556
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 2.7172 - val_loss: 1.0129
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 2.1389 - val_loss: 0.7229
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 1.9233 - val_loss: 0.3393
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 1.7346 - val_loss: 0.8882
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 1.8277 - val_loss: 0.6586
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 1.5591 - val_loss: 0.7607
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 1.5614 - val_loss: 0.2841
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 1.2353 - val_loss: 0.4948
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 1.2656 - val_loss: 0.2453
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 1.1303 - val_loss: 0.2200
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.9998 - val_loss: 0.3145
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 1.0383 - val_loss: 0.1971
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.9177 - val_loss: 0.9053
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 1.0042 - val_loss: 0.1680
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.8165 - val_loss: 0.2524
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.8053 - val_loss: 0.2095
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.7609 - val_loss: 0.1422
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.6541 - val_loss: 0.8241
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 1.0995 - val_loss: 0.1558
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.5294 - val_loss: 0.1332
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.5698 - val_loss: 0.2147
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.5670 - val_loss: 0.4011
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.6653 - val_loss: 0.1411
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.3983 - val_loss: 0.1111
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.3895 - val_loss: 0.2329
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 90ms/step - loss: 0.3718 - val_loss: 0.1967
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.3645 - val_loss: 0.0973
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.3473 - val_loss: 0.1750
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.3831 - val_loss: 0.3196
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.2604 - val_loss: 0.0983
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.2990 - val_loss: 0.2049
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.2218 - val_loss: 0.0978
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.3939 - val_loss: 0.0411
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.1679 - val_loss: 0.0476
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.2425 - val_loss: 0.0259
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.1983 - val_loss: 0.0232
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.1646 - val_loss: 0.0203
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.2022 - val_loss: 0.0175
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.0938 - val_loss: 0.0570
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.1646 - val_loss: 0.1981
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.1884 - val_loss: 0.1556
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.1310 - val_loss: 0.0397
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.0890 - val_loss: 0.0144
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.1312 - val_loss: 0.0437
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.0860 - val_loss: 0.0159
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.2870 - val_loss: 0.0470
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 0.1391 - val_loss: 0.0133
Epoch 56/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.0849 - val_loss: 0.0546
Epoch 57/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.1876 - val_loss: 0.0110
Epoch 58/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.0633 - val_loss: 0.0956
Epoch 59/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.0934 - val_loss: 0.0159
Epoch 60/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.0557 - val_loss: 0.0418
Epoch 61/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.1902 - val_loss: 0.0117
Epoch 62/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.0485 - val_loss: 0.0118
Epoch 63/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.1356 - val_loss: 0.0540
Epoch 64/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.1110 - val_loss: 0.0111
Epoch 65/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.1299 - val_loss: 0.0351
Epoch 66/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 0.0467 - val_loss: 0.4760
Epoch 67/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 0.1946 - val_loss: 0.1004
54/54 ━━━━━━━━━━━━━━━━━━━━ 1s 25ms/step 
Sample raw predictions (after inverse transform and clipping): [0.6814651  0.12724799 3.3139951  1.7466629  3.230381  ]
RMSE =  2.3558202
Validation R-squared for item 1071: 0.1470813751220703
49/49 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=5.0997514724731445
True y_val range (after inverse transform): min=0.0, max=15.0
No description has been provided for this image
-----------------------------------
Current item is  2035
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=14
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7142857142857142
x_eval_time shape before reshape: (1614, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1894, 14)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (224, 20)
store_x_eval.shape: (217, 20)
store_x_eval.shape: (162, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (171, 20)
Model: "sequential_112"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_225 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_130 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_226 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_131 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_111 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 93ms/step - loss: 20651.7949 - val_loss: 796.6826
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 2279.7751 - val_loss: 89.2630
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 312.8403 - val_loss: 4.0923
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 115.8305 - val_loss: 1.7089
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 68.4937 - val_loss: 3.8763
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 40.6137 - val_loss: 1.0619
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 26.6705 - val_loss: 3.3223
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 19.6974 - val_loss: 0.6741
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 16.8798 - val_loss: 0.3387
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 14.2334 - val_loss: 1.6492
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 9.1557 - val_loss: 0.1990
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 8.2573 - val_loss: 0.6116
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 6.7377 - val_loss: 0.5191
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 5.5109 - val_loss: 0.2557
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 4.9593 - val_loss: 0.6626
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 6.0268 - val_loss: 0.0849
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 5.7536 - val_loss: 1.0590
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 5.7745 - val_loss: 2.1848
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 3.2487 - val_loss: 0.0950
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 3.7076 - val_loss: 0.0824
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 2.7866 - val_loss: 1.7842
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 2.5828 - val_loss: 0.0470
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 2.3730 - val_loss: 1.4014
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 2.1942 - val_loss: 0.9276
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 2.3433 - val_loss: 0.0272
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 3.0175 - val_loss: 0.0877
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 1.2516 - val_loss: 0.0209
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.5412 - val_loss: 1.1831
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 1.4704 - val_loss: 0.2301
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 1.4559 - val_loss: 0.1861
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 1.0205 - val_loss: 0.2376
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 0.7650 - val_loss: 0.1679
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 1.0846 - val_loss: 5.5067
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 2.6341 - val_loss: 0.1957
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 1.5159 - val_loss: 0.1665
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 0.6524 - val_loss: 1.3828
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 0.7675 - val_loss: 0.7615
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 26ms/step 
Sample raw predictions (after inverse transform and clipping): [0.23848294 0.         0.51890236 0.         0.24205278]
RMSE =  2.0759268
Validation R-squared for item 2035: -3.9169158935546875
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=24.821575164794922
True y_val range (after inverse transform): min=0.0, max=10.0
No description has been provided for this image
-----------------------------------
Current item is  2370
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=8
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.75
x_eval_time shape before reshape: (1610, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1890, 14)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (210, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (183, 20)
Model: "sequential_113"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_227 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_132 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_228 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_133 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_112 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 94ms/step - loss: 17818.8613 - val_loss: 831.9936
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 1641.6783 - val_loss: 59.3500
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 353.9845 - val_loss: 22.0156
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 139.8264 - val_loss: 8.9056
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 68.3212 - val_loss: 2.9254
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 41.4184 - val_loss: 1.1886
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 23.6783 - val_loss: 0.7856
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 15.4516 - val_loss: 0.5027
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 12.4206 - val_loss: 1.3853
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 8.3620 - val_loss: 0.3995
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 6.1768 - val_loss: 0.6855
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 4.5316 - val_loss: 0.5676
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 3.3418 - val_loss: 0.0924
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 3.0338 - val_loss: 0.2661
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 3.5355 - val_loss: 0.0472
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 3.9357 - val_loss: 0.1400
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 3.3209 - val_loss: 0.0939
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 1.9177 - val_loss: 0.0200
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.4442 - val_loss: 0.2090
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 1.1646 - val_loss: 0.1208
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 0.7904 - val_loss: 0.0316
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.7120 - val_loss: 0.1957
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.7696 - val_loss: 0.0525
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.6064 - val_loss: 0.3776
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.4754 - val_loss: 0.2592
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.6056 - val_loss: 0.4662
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.5564 - val_loss: 0.0321
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 3.0529 - val_loss: 0.0068
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.9064 - val_loss: 0.0169
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.7567 - val_loss: 0.0976
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.5209 - val_loss: 0.0279
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.6305 - val_loss: 0.0063
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.3765 - val_loss: 0.0087
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.3911 - val_loss: 0.0805
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.3041 - val_loss: 0.3032
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.4000 - val_loss: 0.0117
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.4770 - val_loss: 0.0219
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.3881 - val_loss: 0.0417
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.3245 - val_loss: 0.0522
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.3053 - val_loss: 0.0442
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.2699 - val_loss: 0.2168
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.2499 - val_loss: 0.0592
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 25ms/step 
Sample raw predictions (after inverse transform and clipping): [0.08729735 0.03906438 0.         0.42464206 0.65024674]
RMSE =  0.6794611
Validation R-squared for item 2370: -0.2652021646499634
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.5332125425338745
True y_val range (after inverse transform): min=0.0, max=6.0
No description has been provided for this image
-----------------------------------
Current item is  2444
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=7
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5714285714285714
x_eval_time shape before reshape: (1639, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1919, 14)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (188, 20)
Model: "sequential_114"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_229 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_134 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_230 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_135 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_113 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 93ms/step - loss: 16581.0645 - val_loss: 190.1826
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 915.2839 - val_loss: 20.9216
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 149.8952 - val_loss: 51.5123
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 278.2110 - val_loss: 16.6080
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 73.6453 - val_loss: 7.2981
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 44.7855 - val_loss: 2.7861
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 32.1076 - val_loss: 1.7387
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 22.3222 - val_loss: 1.1264
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 16.6120 - val_loss: 1.0069
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 11.3983 - val_loss: 1.7574
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 10.1894 - val_loss: 0.5808
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 8.7203 - val_loss: 0.4331
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 7.7899 - val_loss: 0.2702
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 5.7700 - val_loss: 0.9785
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 5.2027 - val_loss: 1.0221
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 4.1083 - val_loss: 0.1879
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 3.4012 - val_loss: 0.1724
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 2.7934 - val_loss: 0.2747
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 2.5976 - val_loss: 0.4959
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 2.3083 - val_loss: 0.1736
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 2.1654 - val_loss: 0.5358
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 1.7956 - val_loss: 0.1877
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 1.7374 - val_loss: 0.3573
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.4657 - val_loss: 0.0647
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 1.8538 - val_loss: 0.0267
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 1.6226 - val_loss: 0.0344
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 1.2934 - val_loss: 0.0373
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 1.0960 - val_loss: 0.0308
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.9703 - val_loss: 0.1786
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 0.9248 - val_loss: 0.0728
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.7469 - val_loss: 0.5441
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 0.8554 - val_loss: 0.1685
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.6894 - val_loss: 0.3928
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.7402 - val_loss: 0.0453
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.5666 - val_loss: 0.3598
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 25ms/step 
Sample raw predictions (after inverse transform and clipping): [0.11784206 2.7089045  0.06941345 0.5390756  0.19733861]
RMSE =  1.0155368
Validation R-squared for item 2444: -1.353858470916748
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=5.2807793617248535
True y_val range (after inverse transform): min=0.0, max=4.0
No description has been provided for this image
-----------------------------------
Current item is  1339
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=102
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5980392156862745
x_eval_time shape before reshape: (1695, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1975, 14)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (193, 20)
Model: "sequential_115"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_231 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_136 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_232 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_137 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_114 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 96ms/step - loss: 48266.6172 - val_loss: 3645.6582
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 10425.1650 - val_loss: 1423.9637
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 1290.5101 - val_loss: 195.6954
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 563.1725 - val_loss: 58.0825
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 325.3034 - val_loss: 38.7630
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 204.3329 - val_loss: 39.2156
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 162.8071 - val_loss: 7.1148
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 118.0632 - val_loss: 3.7446
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 68.9065 - val_loss: 3.9367
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 51.6228 - val_loss: 3.2063
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 34.6441 - val_loss: 1.5169
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 26.7913 - val_loss: 8.5597
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 20.6993 - val_loss: 2.6844
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 14.7201 - val_loss: 0.9679
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 12.1415 - val_loss: 3.0672
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 9.9589 - val_loss: 5.2705
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 8.2209 - val_loss: 0.8884
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 7.0101 - val_loss: 1.6244
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 7.1893 - val_loss: 3.3497
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 5.6034 - val_loss: 0.5318
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 4.6803 - val_loss: 1.3362
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 4.2341 - val_loss: 5.0382
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 4.5042 - val_loss: 0.4177
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 2.9843 - val_loss: 1.3404
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 3.5364 - val_loss: 0.5387
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 2.6791 - val_loss: 3.1173
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 3.5747 - val_loss: 2.6064
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 2.4079 - val_loss: 0.2929
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 2.6620 - val_loss: 2.0416
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 2.3798 - val_loss: 0.6102
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 2.4935 - val_loss: 0.3566
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 1.7280 - val_loss: 0.2883
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.8638 - val_loss: 0.0983
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 2.5438 - val_loss: 3.3541
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 2.2374 - val_loss: 0.4582
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 2.1242 - val_loss: 0.1501
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 1.8087 - val_loss: 0.3771
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 1.7310 - val_loss: 0.8021
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 1.6239 - val_loss: 0.1434
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 1.4529 - val_loss: 0.6862
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 1.5947 - val_loss: 6.4290
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 2.9599 - val_loss: 0.1073
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 1.9849 - val_loss: 1.1349
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 26ms/step 
Sample raw predictions (after inverse transform and clipping): [0.        8.751868  2.5300403 6.2340503 0.       ]
RMSE =  30.05228
Validation R-squared for item 1339: -12.355067253112793
53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=302.6733703613281
True y_val range (after inverse transform): min=0.0, max=61.0
No description has been provided for this image
-----------------------------------
Current item is  1168
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=18
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8888888888888888
x_eval_time shape before reshape: (1602, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1882, 14)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (209, 20)
Model: "sequential_116"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_233 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_138 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_234 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_139 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_115 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 97ms/step - loss: 19570.7969 - val_loss: 11738.8379
Epoch 2/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 7685.4756 - val_loss: 66.3188
Epoch 3/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 405.8290 - val_loss: 31.9284
Epoch 4/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 185.6304 - val_loss: 15.6737
Epoch 5/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 96.1528 - val_loss: 9.9086
Epoch 6/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 53.6422 - val_loss: 6.2851
Epoch 7/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 35.6351 - val_loss: 4.9108
Epoch 8/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 24.6292 - val_loss: 4.3806
Epoch 9/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 16.7789 - val_loss: 3.3255
Epoch 10/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 12.5379 - val_loss: 2.2650
Epoch 11/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 8.4458 - val_loss: 1.3489
Epoch 12/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 6.6212 - val_loss: 0.7806
Epoch 13/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 6.0906 - val_loss: 0.6395
Epoch 14/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 4.1261 - val_loss: 0.7412
Epoch 15/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 3.9291 - val_loss: 0.2869
Epoch 16/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 3.1410 - val_loss: 0.1868
Epoch 17/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 2.5537 - val_loss: 0.3546
Epoch 18/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 2.3000 - val_loss: 0.1579
Epoch 19/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 1.6333 - val_loss: 0.1374
Epoch 20/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 1.8197 - val_loss: 0.0987
Epoch 21/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 1.3228 - val_loss: 0.1209
Epoch 22/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 1.1461 - val_loss: 0.0911
Epoch 23/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 1.0987 - val_loss: 0.0590
Epoch 24/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 1.1264 - val_loss: 0.0313
Epoch 25/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.9990 - val_loss: 0.0411
Epoch 26/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.6592 - val_loss: 0.1086
Epoch 27/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.6286 - val_loss: 0.0196
Epoch 28/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.8309 - val_loss: 0.0491
Epoch 29/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.5718 - val_loss: 0.2921
Epoch 30/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 0.4829 - val_loss: 0.0512
Epoch 31/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.3994 - val_loss: 0.0619
Epoch 32/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.4614 - val_loss: 0.0141
Epoch 33/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.3741 - val_loss: 0.1981
Epoch 34/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.4525 - val_loss: 0.0279
Epoch 35/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.3115 - val_loss: 0.2109
Epoch 36/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.3128 - val_loss: 0.4612
Epoch 37/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.3801 - val_loss: 0.0084
Epoch 38/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 0.3326 - val_loss: 0.1138
Epoch 39/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.2736 - val_loss: 0.0094
Epoch 40/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.2285 - val_loss: 0.0085
Epoch 41/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.2554 - val_loss: 0.0470
Epoch 42/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 0.2053 - val_loss: 0.0087
Epoch 43/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.1690 - val_loss: 0.1727
Epoch 44/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 0.1987 - val_loss: 0.0483
Epoch 45/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 0.3254 - val_loss: 0.0170
Epoch 46/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.1972 - val_loss: 0.0076
Epoch 47/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.1457 - val_loss: 0.0233
Epoch 48/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.1252 - val_loss: 0.0134
Epoch 49/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.3113 - val_loss: 0.0855
Epoch 50/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.1862 - val_loss: 0.0577
Epoch 51/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.2535 - val_loss: 0.0063
Epoch 52/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.1584 - val_loss: 0.0175
Epoch 53/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.1895 - val_loss: 0.0688
Epoch 54/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.2446 - val_loss: 0.0387
Epoch 55/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.1174 - val_loss: 0.0084
Epoch 56/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.1810 - val_loss: 0.4725
Epoch 57/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 0.3985 - val_loss: 0.0148
Epoch 58/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.1358 - val_loss: 0.0324
Epoch 59/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.2215 - val_loss: 0.3126
Epoch 60/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.5003 - val_loss: 0.0090
Epoch 61/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 0.1130 - val_loss: 0.8196
50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step 
Sample raw predictions (after inverse transform and clipping): [0.636791  0.7461896 0.8714721 1.0400041 1.0651879]
RMSE =  1.5499761
Validation R-squared for item 1168: -0.16369616985321045
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 25ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=13.222320556640625
True y_val range (after inverse transform): min=0.0, max=16.0
No description has been provided for this image
-----------------------------------
Current item is  2470
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=12
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.75
x_eval_time shape before reshape: (1636, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1916, 14)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (186, 20)
Model: "sequential_117"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_235 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_140 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_236 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_141 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_116 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 98ms/step - loss: 69991.3125 - val_loss: 15183.8691
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 9160.6084 - val_loss: 449.0584
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1824.4952 - val_loss: 321.6829
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 930.6652 - val_loss: 160.3697
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 577.0227 - val_loss: 57.4398
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 361.3781 - val_loss: 31.6635
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 200.3084 - val_loss: 18.2972
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 142.6639 - val_loss: 10.3974
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 89.9479 - val_loss: 12.4704
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 102.5629 - val_loss: 12.0472
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 85.4553 - val_loss: 9.2528
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 72.8317 - val_loss: 6.5390
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 62.1694 - val_loss: 9.7877
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 48.7639 - val_loss: 5.2090
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 42.7681 - val_loss: 4.4670
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 35.7223 - val_loss: 4.4840
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 30.6255 - val_loss: 3.5240
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 27.7900 - val_loss: 3.1768
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 23.5386 - val_loss: 2.8685
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 20.8099 - val_loss: 2.2918
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 18.4603 - val_loss: 1.9113
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 14.3877 - val_loss: 1.5883
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 12.2412 - val_loss: 4.3001
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 11.7326 - val_loss: 1.7256
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 9.5224 - val_loss: 1.1362
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 8.8460 - val_loss: 1.1270
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 6.7249 - val_loss: 1.3421
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 5.8260 - val_loss: 1.0774
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 5.1805 - val_loss: 0.4044
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 4.5108 - val_loss: 1.7197
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 3.9182 - val_loss: 0.2620
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 3.1207 - val_loss: 0.9958
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 2.9656 - val_loss: 0.6852
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 3.1922 - val_loss: 0.2326
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 2.4435 - val_loss: 0.1041
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.8255 - val_loss: 0.0865
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.5131 - val_loss: 0.1211
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.7374 - val_loss: 0.1218
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 1.3013 - val_loss: 0.1137
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.1680 - val_loss: 0.2849
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.9207 - val_loss: 0.1323
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.0218 - val_loss: 0.0798
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.0051 - val_loss: 0.0522
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.0271 - val_loss: 0.4470
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.1497 - val_loss: 0.0535
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.0266 - val_loss: 0.6321
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.8191 - val_loss: 1.0480
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.8992 - val_loss: 0.0361
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.6254 - val_loss: 0.0444
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.9233 - val_loss: 0.5470
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 1.7033 - val_loss: 0.2173
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.6473 - val_loss: 1.1025
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.7226 - val_loss: 0.0278
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.7034 - val_loss: 0.0788
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 1.0411 - val_loss: 0.5015
Epoch 56/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.4824 - val_loss: 0.7192
Epoch 57/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.7612 - val_loss: 0.1330
Epoch 58/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.6391 - val_loss: 1.5021
Epoch 59/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.4700 - val_loss: 0.4144
Epoch 60/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.0581 - val_loss: 0.1175
Epoch 61/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.3805 - val_loss: 0.3919
Epoch 62/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.6586 - val_loss: 0.8819
Epoch 63/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.9893 - val_loss: 0.0228
Epoch 64/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.3665 - val_loss: 0.0333
Epoch 65/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.4268 - val_loss: 0.0352
Epoch 66/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.5547 - val_loss: 0.0227
Epoch 67/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.3150 - val_loss: 0.7664
Epoch 68/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.7458 - val_loss: 1.2534
Epoch 69/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.5421 - val_loss: 0.8052
Epoch 70/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.8251 - val_loss: 0.1088
Epoch 71/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.2684 - val_loss: 0.0958
Epoch 72/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.9398 - val_loss: 0.0173
Epoch 73/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.3159 - val_loss: 0.3028
Epoch 74/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.4475 - val_loss: 0.6820
Epoch 75/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.7987 - val_loss: 1.3153
Epoch 76/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.3978 - val_loss: 1.8486
Epoch 77/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.5147 - val_loss: 1.1782
Epoch 78/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.8770 - val_loss: 1.2221
Epoch 79/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.3227 - val_loss: 0.0150
Epoch 80/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.2643 - val_loss: 0.0313
Epoch 81/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.2237 - val_loss: 1.3545
Epoch 82/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.6647 - val_loss: 0.0166
Epoch 83/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.3633 - val_loss: 0.2935
Epoch 84/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.5912 - val_loss: 0.0324
Epoch 85/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.5718 - val_loss: 0.0349
Epoch 86/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.3092 - val_loss: 0.2277
Epoch 87/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.3683 - val_loss: 0.2744
Epoch 88/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.1930 - val_loss: 0.0483
Epoch 89/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.4699 - val_loss: 0.0159
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 25ms/step 
Sample raw predictions (after inverse transform and clipping): [1.8019457 1.6105288 0.9620671 1.0381957 1.0995377]
RMSE =  1.5605145
Validation R-squared for item 2470: -0.23828232288360596
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 28ms/step
Predicted y_val range (after inverse transform and clipping): min=0.4473133087158203, max=3.2438201904296875
True y_val range (after inverse transform): min=0.0, max=9.0
No description has been provided for this image
-----------------------------------
Current item is  2451
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=9
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7777777777777777
x_eval_time shape before reshape: (1675, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1955, 14)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (215, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (189, 20)
Model: "sequential_118"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_237 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_142 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_238 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_143 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_117 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 95ms/step - loss: 6962.4102 - val_loss: 97.0596
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 128.7729 - val_loss: 1.3564
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 18.5333 - val_loss: 0.3377
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 9.5818 - val_loss: 0.6846
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 8.1876 - val_loss: 0.6562
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 5.7067 - val_loss: 0.1314
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 3.7670 - val_loss: 0.2134
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 3.1436 - val_loss: 0.1660
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.9473 - val_loss: 0.7313
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.9534 - val_loss: 0.0603
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.5748 - val_loss: 0.1910
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.6019 - val_loss: 0.1940
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.5782 - val_loss: 0.2581
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.4203 - val_loss: 0.1147
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.8771 - val_loss: 0.0090
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.1411 - val_loss: 0.0728
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.9177 - val_loss: 0.0297
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.7146 - val_loss: 0.8223
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.9136 - val_loss: 0.0105
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.4669 - val_loss: 0.2839
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 0.4502 - val_loss: 0.1578
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.9200 - val_loss: 0.0308
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.0981 - val_loss: 0.0058
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.4483 - val_loss: 0.0800
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.7885 - val_loss: 0.0184
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.7932 - val_loss: 0.1447
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 0.4078 - val_loss: 0.0105
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.2758 - val_loss: 0.1110
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.3079 - val_loss: 0.3821
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.3680 - val_loss: 0.1388
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.4072 - val_loss: 0.3109
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.5221 - val_loss: 0.0067
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.1865 - val_loss: 0.5433
49/49 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step 
Sample raw predictions (after inverse transform and clipping): [0.47592464 0.55956143 0.8469352  0.31684068 0.5582867 ]
RMSE =  0.7544475
Validation R-squared for item 2451: -0.0637444257736206
53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=3.654181718826294
True y_val range (after inverse transform): min=0.0, max=7.0
No description has been provided for this image
-----------------------------------
Current item is  2092
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=4
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.25
x_eval_time shape before reshape: (1620, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1900, 14)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (177, 20)
Model: "sequential_119"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_239 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_144 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_240 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_145 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_118 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 101ms/step - loss: 123705.4062 - val_loss: 53306.6523
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 151662.9531 - val_loss: 883.6946
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 5679.0498 - val_loss: 372.6973
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 1360.1938 - val_loss: 172.1302
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 901.4240 - val_loss: 42.3644
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 192.0967 - val_loss: 22.3896
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 97.5519 - val_loss: 16.9523
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 70.5634 - val_loss: 13.4440
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 76.5573 - val_loss: 7.3779
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 45.8745 - val_loss: 3.7886
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 32.6827 - val_loss: 3.7373
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 26.4435 - val_loss: 3.2457
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 20.8646 - val_loss: 3.6593
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 20.4365 - val_loss: 2.2921
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 19.2664 - val_loss: 3.0933
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 16.4647 - val_loss: 1.5802
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 13.2117 - val_loss: 1.6984
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 11.8243 - val_loss: 1.0158
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 10.0006 - val_loss: 1.1602
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 9.9033 - val_loss: 0.9925
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 8.7226 - val_loss: 0.7267
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 7.5389 - val_loss: 0.4587
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 7.7392 - val_loss: 2.4709
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 15.6271 - val_loss: 0.2013
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 4.5824 - val_loss: 0.0217
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 1.6134 - val_loss: 0.1105
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 1.5147 - val_loss: 0.1144
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 0.9282 - val_loss: 0.0920
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 0.9057 - val_loss: 0.0394
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.6249 - val_loss: 0.1022
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.6313 - val_loss: 0.0899
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.5981 - val_loss: 0.0330
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 0.5022 - val_loss: 0.0472
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.4917 - val_loss: 0.0268
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.4262 - val_loss: 0.0162
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.2988 - val_loss: 0.0127
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 0.3023 - val_loss: 0.0155
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 0.2980 - val_loss: 0.0170
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 0.3025 - val_loss: 0.0192
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 0.2899 - val_loss: 0.0438
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.3153 - val_loss: 0.0279
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 0.2733 - val_loss: 0.0453
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 0.2725 - val_loss: 0.0273
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.2695 - val_loss: 0.0399
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.2401 - val_loss: 0.0308
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.2271 - val_loss: 0.0455
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step 
Sample raw predictions (after inverse transform and clipping): [0.18846442 0.         0.         0.17134584 0.11959328]
RMSE =  0.3991997
Validation R-squared for item 2092: -0.6738653182983398
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 26ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.1492955684661865
True y_val range (after inverse transform): min=0.0, max=2.0
No description has been provided for this image
-----------------------------------
Current item is  2346
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=13
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.2307692307692308
x_eval_time shape before reshape: (1630, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1910, 14)
store_x_eval.shape: (167, 20)
store_x_eval.shape: (210, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (208, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (162, 20)
Model: "sequential_120"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_241 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_146 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_242 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_147 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_119 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 99ms/step - loss: 4953.7446 - val_loss: 282.4979
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 373.2223 - val_loss: 1.1901
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 10.1258 - val_loss: 0.2302
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 3.6697 - val_loss: 0.0934
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 2.8493 - val_loss: 0.0713
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 2.3891 - val_loss: 0.1119
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 1.6545 - val_loss: 0.1289
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 1.3392 - val_loss: 0.0314
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 1.2406 - val_loss: 0.1396
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.5891 - val_loss: 0.0259
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.5974 - val_loss: 0.0224
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.4662 - val_loss: 0.0195
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.6016 - val_loss: 0.0396
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.3810 - val_loss: 0.0971
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.4921 - val_loss: 0.1130
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.5080 - val_loss: 0.0159
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.4254 - val_loss: 0.0181
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.3240 - val_loss: 0.0575
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.3125 - val_loss: 0.0423
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1624 - val_loss: 0.0704
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.3577 - val_loss: 0.0146
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1807 - val_loss: 0.0322
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.2139 - val_loss: 0.0530
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.2352 - val_loss: 0.0202
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.2267 - val_loss: 0.0514
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1636 - val_loss: 0.0159
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.1858 - val_loss: 0.0163
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.2448 - val_loss: 0.1007
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.1855 - val_loss: 0.0135
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1156 - val_loss: 0.0169
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1337 - val_loss: 0.0820
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1798 - val_loss: 0.0817
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1606 - val_loss: 0.0714
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1598 - val_loss: 0.0597
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1736 - val_loss: 0.0221
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1004 - val_loss: 0.0319
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.0571 - val_loss: 0.0154
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.0769 - val_loss: 0.0150
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1112 - val_loss: 0.0172
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 29ms/step 
Sample raw predictions (after inverse transform and clipping): [1.2925555 0.6595985 0.8774176 1.0011544 0.7275284]
RMSE =  1.4093059
Validation R-squared for item 2346: -0.2229553461074829
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 26ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=11.396166801452637
True y_val range (after inverse transform): min=0.0, max=16.0
No description has been provided for this image
-----------------------------------
Current item is  1385
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=57
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.1403508771929824
x_eval_time shape before reshape: (1682, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1962, 14)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (193, 20)
Model: "sequential_121"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_243 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_148 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_244 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_149 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_120 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 103ms/step - loss: 20203.2441 - val_loss: 466.9173
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 1251.3331 - val_loss: 73.0737
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 158.7083 - val_loss: 18.4454
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 27.2422 - val_loss: 3.0997
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 10.9069 - val_loss: 2.6540
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 5.9858 - val_loss: 1.6445
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 2.1427 - val_loss: 0.7796
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 1.3410 - val_loss: 0.4213
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 1.0756 - val_loss: 0.9193
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 1.0710 - val_loss: 0.3196
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.7817 - val_loss: 0.1159
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.7946 - val_loss: 0.7174
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 1.0420 - val_loss: 0.0397
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.5244 - val_loss: 0.6609
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.6773 - val_loss: 0.3428
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.4954 - val_loss: 0.1682
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.4460 - val_loss: 1.0376
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.9936 - val_loss: 0.1772
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.4319 - val_loss: 0.0715
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.3800 - val_loss: 0.1537
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.3044 - val_loss: 0.0297
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.2525 - val_loss: 0.0656
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.3886 - val_loss: 0.0909
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.3597 - val_loss: 0.4006
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.2660 - val_loss: 0.0217
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.2471 - val_loss: 0.0105
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.4025 - val_loss: 1.1108
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.5336 - val_loss: 0.0355
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.1639 - val_loss: 0.1529
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.2582 - val_loss: 0.0854
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.1788 - val_loss: 0.0048
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.2093 - val_loss: 0.0753
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.1089 - val_loss: 0.3914
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.8121 - val_loss: 0.0389
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.4543 - val_loss: 0.0650
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.1021 - val_loss: 0.0150
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.2026 - val_loss: 0.0279
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.0714 - val_loss: 0.0707
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.3425 - val_loss: 0.4742
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.1192 - val_loss: 0.0157
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.1543 - val_loss: 0.2907
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 30ms/step 
Sample raw predictions (after inverse transform and clipping): [0.       8.187924 0.       8.680041 5.562109]
RMSE =  4.395315
Validation R-squared for item 1385: -0.773018479347229
53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 28ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=12.401473045349121
True y_val range (after inverse transform): min=0.0, max=65.0
No description has been provided for this image
-----------------------------------
Current item is  1418
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=82
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.975609756097561
x_eval_time shape before reshape: (1622, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1902, 14)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (210, 20)
store_x_eval.shape: (169, 20)
store_x_eval.shape: (168, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (212, 20)
Model: "sequential_122"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_245 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_150 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_246 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_151 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_121 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 103ms/step - loss: 33253.5156 - val_loss: 753.2000
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 1835.3767 - val_loss: 108.0670
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 370.0514 - val_loss: 33.2398
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 189.1287 - val_loss: 34.0389
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 107.7196 - val_loss: 17.3671
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 85.1613 - val_loss: 9.4057
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 66.9136 - val_loss: 7.2616
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 47.6285 - val_loss: 13.8233
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 38.1408 - val_loss: 3.2651
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 29.8418 - val_loss: 2.6803
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 21.5605 - val_loss: 1.0225
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 6.6670 - val_loss: 0.6910
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 2.8295 - val_loss: 0.6176
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 2.0769 - val_loss: 0.7665
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 3.0716 - val_loss: 0.1316
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 1.1946 - val_loss: 0.0699
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 1.0654 - val_loss: 0.1056
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.7720 - val_loss: 0.0883
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.8978 - val_loss: 0.2460
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.8591 - val_loss: 0.1841
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.5533 - val_loss: 0.1297
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.3432 - val_loss: 0.3009
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.3871 - val_loss: 0.2513
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.2799 - val_loss: 0.0464
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.2746 - val_loss: 0.0643
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.2539 - val_loss: 0.1706
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.3925 - val_loss: 0.0672
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.1830 - val_loss: 0.0690
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.5606 - val_loss: 0.3301
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.5521 - val_loss: 0.0163
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.3631 - val_loss: 0.0151
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 1.6377 - val_loss: 0.0345
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.2353 - val_loss: 0.4756
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.4941 - val_loss: 0.0118
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.2196 - val_loss: 0.0804
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.3725 - val_loss: 0.0147
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.1637 - val_loss: 0.1371
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.1303 - val_loss: 0.0229
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.3142 - val_loss: 0.0878
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 7.9537 - val_loss: 0.0367
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.3793 - val_loss: 0.0206
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.1645 - val_loss: 0.0151
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.1468 - val_loss: 0.0990
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.0843 - val_loss: 0.0357
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 30ms/step 
Sample raw predictions (after inverse transform and clipping): [12.608353  16.859123  10.918046   1.6098084  1.5930306]
RMSE =  9.554832
Validation R-squared for item 1418: -0.34960055351257324
51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=28.063865661621094
True y_val range (after inverse transform): min=0.0, max=80.0
No description has been provided for this image
-----------------------------------
Current item is  2218
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=4
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.0
x_eval_time shape before reshape: (1674, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1954, 14)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (217, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (162, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (218, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (191, 20)
Model: "sequential_123"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_247 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_152 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_248 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_153 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_122 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 104ms/step - loss: 26117.4531 - val_loss: 1021.0290
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 2339.4175 - val_loss: 100.6271
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 493.4540 - val_loss: 55.7553
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 168.6380 - val_loss: 27.8086
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 94.5537 - val_loss: 8.1227
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 44.8432 - val_loss: 8.1789
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 20.2939 - val_loss: 5.6488
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 12.1928 - val_loss: 4.0152
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 8.2380 - val_loss: 5.2129
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 6.1950 - val_loss: 2.0753
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 4.1176 - val_loss: 2.6222
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 3.0636 - val_loss: 0.3547
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 1.6137 - val_loss: 2.8439
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 1.2795 - val_loss: 1.2249
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.9136 - val_loss: 1.5028
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.6644 - val_loss: 0.5135
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.5498 - val_loss: 0.3088
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.4956 - val_loss: 0.4442
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.4006 - val_loss: 0.2699
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.4868 - val_loss: 0.2363
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.3439 - val_loss: 0.7047
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.3556 - val_loss: 0.1047
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.2714 - val_loss: 0.0146
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.2201 - val_loss: 1.1833
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.6891 - val_loss: 0.0188
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.2898 - val_loss: 0.1904
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.3585 - val_loss: 0.9802
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.3860 - val_loss: 0.1198
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.2565 - val_loss: 0.0498
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.3268 - val_loss: 0.0122
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.3958 - val_loss: 0.0448
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.1953 - val_loss: 3.1627
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.6825 - val_loss: 1.7181
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.5474 - val_loss: 0.0481
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.2810 - val_loss: 0.0160
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.3253 - val_loss: 0.2027
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.2654 - val_loss: 0.1589
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.2950 - val_loss: 0.8831
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.3162 - val_loss: 0.2269
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.3514 - val_loss: 0.2898
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 30ms/step 
Sample raw predictions (after inverse transform and clipping): [0.46083382 0.47313693 0.41631088 0.40143654 0.4207708 ]
RMSE =  0.47188592
Validation R-squared for item 2218: -0.48597514629364014
53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.8005332350730896
True y_val range (after inverse transform): min=0.0, max=4.0
No description has been provided for this image
-----------------------------------
Current item is  1372
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=24
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.75
x_eval_time shape before reshape: (1691, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1971, 14)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (212, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (190, 20)
Model: "sequential_124"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_249 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_154 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_250 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_155 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_123 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 12s 104ms/step - loss: 5724.9556 - val_loss: 31.0461
Epoch 2/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 78.7968 - val_loss: 4.7368
Epoch 3/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 18.5161 - val_loss: 2.0129
Epoch 4/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 7.3736 - val_loss: 0.2345
Epoch 5/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 2.2392 - val_loss: 0.0856
Epoch 6/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 1.2214 - val_loss: 0.0450
Epoch 7/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.6485 - val_loss: 0.0197
Epoch 8/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.4221 - val_loss: 0.0330
Epoch 9/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.4275 - val_loss: 0.0372
Epoch 10/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.2816 - val_loss: 0.0394
Epoch 11/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.1987 - val_loss: 0.0046
Epoch 12/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.1332 - val_loss: 0.0110
Epoch 13/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.1902 - val_loss: 0.0427
Epoch 14/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.3585 - val_loss: 0.0482
Epoch 15/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.1996 - val_loss: 0.0031
Epoch 16/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.0895 - val_loss: 0.0028
Epoch 17/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.0860 - val_loss: 0.0040
Epoch 18/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.0475 - val_loss: 0.0474
Epoch 19/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.1608 - val_loss: 0.0364
Epoch 20/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.1435 - val_loss: 0.0269
Epoch 21/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.0264 - val_loss: 0.0271
Epoch 22/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.0416 - val_loss: 0.0081
Epoch 23/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.0479 - val_loss: 0.0313
Epoch 24/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.0604 - val_loss: 0.0035
Epoch 25/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.0315 - val_loss: 0.0212
Epoch 26/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.0384 - val_loss: 0.0121
53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step 
Sample raw predictions (after inverse transform and clipping): [1.6629157 1.7624677 1.6999266 1.8415756 1.8864608]
RMSE =  1.9634923
Validation R-squared for item 1372: -0.02215099334716797
53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step
Predicted y_val range (after inverse transform and clipping): min=0.02439943514764309, max=3.868675708770752
True y_val range (after inverse transform): min=0.0, max=18.0
No description has been provided for this image
-----------------------------------
Current item is  2091
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=5
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6000000000000001
x_eval_time shape before reshape: (1648, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1928, 14)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (192, 20)
Model: "sequential_125"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_251 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_156 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_252 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_157 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_124 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 101ms/step - loss: 2399.5427 - val_loss: 5.9515
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 33.9086 - val_loss: 3.0762
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 9.5099 - val_loss: 0.2979
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 4.3145 - val_loss: 1.2266
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 2.8398 - val_loss: 0.1496
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 1.7588 - val_loss: 0.0381
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 1.1148 - val_loss: 0.0973
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 1.0644 - val_loss: 0.4346
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.6968 - val_loss: 0.0190
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.4938 - val_loss: 0.0293
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.4478 - val_loss: 0.0765
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.3174 - val_loss: 0.1364
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.4236 - val_loss: 0.7585
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.2486 - val_loss: 0.8574
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.3788 - val_loss: 0.1644
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.2394 - val_loss: 0.6668
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.2741 - val_loss: 0.1288
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.1347 - val_loss: 0.0957
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.1741 - val_loss: 0.0660
50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step 
Sample raw predictions (after inverse transform and clipping): [0.22082053 0.         0.37015793 0.         0.02117402]
RMSE =  0.615649
Validation R-squared for item 2091: -2.034963369369507
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=2.7173492908477783
True y_val range (after inverse transform): min=0.0, max=3.0
No description has been provided for this image
-----------------------------------
Current item is  1321
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=48
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.47916666666666663
x_eval_time shape before reshape: (1678, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1958, 14)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (173, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (214, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (196, 20)
Model: "sequential_126"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_253 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_158 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_254 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_159 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_125 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 107ms/step - loss: 26642.7246 - val_loss: 10016.4141
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 8470.3135 - val_loss: 285.4241
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 792.8690 - val_loss: 52.5792
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 329.8942 - val_loss: 59.1814
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 95.6770 - val_loss: 13.1222
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 34.2644 - val_loss: 3.8331
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 13.5210 - val_loss: 2.0641
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 9.1096 - val_loss: 3.1732
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 7.3223 - val_loss: 1.3718
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 5.8122 - val_loss: 1.6456
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 4.9873 - val_loss: 1.5436
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 4.1322 - val_loss: 0.5961
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 3.9847 - val_loss: 1.6482
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 108ms/step - loss: 3.5734 - val_loss: 0.4655
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 3.2045 - val_loss: 1.3978
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 2.3909 - val_loss: 0.2719
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 2.1701 - val_loss: 0.3832
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 2.0454 - val_loss: 0.1440
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 2.3097 - val_loss: 0.3282
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 2.1508 - val_loss: 0.4985
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 18.8430 - val_loss: 0.0964
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 1.6706 - val_loss: 0.1185
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 1.3081 - val_loss: 0.2193
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 1.5176 - val_loss: 0.0724
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.2519 - val_loss: 0.3847
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 1.2598 - val_loss: 0.2339
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.0821 - val_loss: 2.4954
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 2.2943 - val_loss: 0.2131
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.0847 - val_loss: 0.1608
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.0928 - val_loss: 0.2702
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.9180 - val_loss: 0.0316
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.8756 - val_loss: 0.4019
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.8001 - val_loss: 0.0905
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 0.7809 - val_loss: 0.4150
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 0.8066 - val_loss: 0.3795
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.7435 - val_loss: 0.0259
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.9482 - val_loss: 0.8153
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.7181 - val_loss: 0.7545
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 0.7100 - val_loss: 0.0172
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 0.5606 - val_loss: 0.0126
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.6853 - val_loss: 1.0255
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.0428 - val_loss: 0.2581
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 0.5939 - val_loss: 0.0819
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.6007 - val_loss: 0.0249
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.6249 - val_loss: 0.0767
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.5363 - val_loss: 0.2901
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.6112 - val_loss: 0.0102
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 0.5338 - val_loss: 0.0613
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.6167 - val_loss: 0.1932
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.6320 - val_loss: 0.1256
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.4279 - val_loss: 0.0416
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.7995 - val_loss: 0.0445
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 0.4439 - val_loss: 0.1581
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.3824 - val_loss: 0.0527
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.3781 - val_loss: 0.0122
Epoch 56/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.3610 - val_loss: 0.1865
Epoch 57/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.3452 - val_loss: 0.0373
50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step 
Sample raw predictions (after inverse transform and clipping): [0.2769277 0.        2.7946901 7.0255384 2.791136 ]
RMSE =  4.542462
Validation R-squared for item 1321: -2.18985652923584
53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 29ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=24.288002014160156
True y_val range (after inverse transform): min=0.0, max=23.0
No description has been provided for this image
-----------------------------------
Current item is  1060
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=4
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5
x_eval_time shape before reshape: (1638, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1918, 14)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (208, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (191, 20)
Model: "sequential_127"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_255 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_160 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_256 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_161 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_126 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 107ms/step - loss: 42605.7500 - val_loss: 81669.3984
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 104442.1250 - val_loss: 371.5465
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 5457.7129 - val_loss: 186.1731
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 3335.4199 - val_loss: 309.5009
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 839.9453 - val_loss: 181.3387
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 521.3579 - val_loss: 70.6890
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 336.7665 - val_loss: 59.7723
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 108ms/step - loss: 255.8872 - val_loss: 36.1934
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 108ms/step - loss: 174.8770 - val_loss: 16.5178
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 117.3841 - val_loss: 16.3701
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 91.1494 - val_loss: 13.6119
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 79.5953 - val_loss: 8.6184
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 67.7404 - val_loss: 20.8481
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 59.4776 - val_loss: 5.3356
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 48.1804 - val_loss: 4.9601
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 44.5957 - val_loss: 6.1596
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 38.6015 - val_loss: 8.5011
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 34.7414 - val_loss: 7.7797
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 29.8312 - val_loss: 4.0536
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 27.2458 - val_loss: 5.0275
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 23.1728 - val_loss: 3.0905
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 19.2566 - val_loss: 3.3138
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 18.3725 - val_loss: 2.7668
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 16.1136 - val_loss: 4.3245
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 15.0155 - val_loss: 2.3340
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 15.2844 - val_loss: 3.6990
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 12.7913 - val_loss: 2.4430
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 13.7159 - val_loss: 1.5232
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 10.6155 - val_loss: 1.3569
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 9.2333 - val_loss: 1.3992
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 9.1264 - val_loss: 1.4151
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 7.7125 - val_loss: 1.1428
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 6.6515 - val_loss: 1.2609
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 6.6926 - val_loss: 1.0533
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 6.1642 - val_loss: 1.0142
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 5.7694 - val_loss: 0.8133
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 7.6656 - val_loss: 1.9801
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 5.1468 - val_loss: 0.6259
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 3.9052 - val_loss: 1.6552
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 3.5883 - val_loss: 0.8517
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 3.6176 - val_loss: 0.4544
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 3.2406 - val_loss: 1.1138
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 2.7879 - val_loss: 3.5464
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 3.3240 - val_loss: 3.2578
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 3.4700 - val_loss: 0.4072
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 2.5223 - val_loss: 0.1964
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.7702 - val_loss: 0.3169
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 1.8534 - val_loss: 0.2098
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.5688 - val_loss: 0.5105
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.5187 - val_loss: 0.1798
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.1653 - val_loss: 0.1694
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 2.9463 - val_loss: 0.3497
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.9548 - val_loss: 0.6854
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.8543 - val_loss: 0.7225
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.9625 - val_loss: 0.1476
Epoch 56/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.6544 - val_loss: 0.1264
Epoch 57/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.6192 - val_loss: 0.0367
Epoch 58/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.5377 - val_loss: 0.0290
Epoch 59/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.4534 - val_loss: 0.0201
Epoch 60/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.5571 - val_loss: 0.0922
Epoch 61/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.6980 - val_loss: 0.1432
Epoch 62/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.6835 - val_loss: 1.1766
Epoch 63/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.6415 - val_loss: 1.7632
Epoch 64/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 3.3025 - val_loss: 0.2744
Epoch 65/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.8255 - val_loss: 0.0910
Epoch 66/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.3794 - val_loss: 0.0420
Epoch 67/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.5802 - val_loss: 0.3924
Epoch 68/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.6328 - val_loss: 0.5087
Epoch 69/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.2022 - val_loss: 0.0140
Epoch 70/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.4148 - val_loss: 0.0119
Epoch 71/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 2.4880 - val_loss: 0.6624
Epoch 72/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.6289 - val_loss: 0.0709
Epoch 73/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 4.8814 - val_loss: 0.1735
Epoch 74/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.2851 - val_loss: 0.1752
Epoch 75/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 1.7202 - val_loss: 0.0378
Epoch 76/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.5379 - val_loss: 12.3370
Epoch 77/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 4.2169 - val_loss: 0.0927
Epoch 78/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.2617 - val_loss: 0.8169
Epoch 79/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.3088 - val_loss: 0.1503
Epoch 80/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.1835 - val_loss: 7.5350
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step 
Sample raw predictions (after inverse transform and clipping): [0. 0. 0. 0. 0.]
RMSE =  0.24507123
Validation R-squared for item 1060: -0.0482640266418457
52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 28ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.2094658762216568
True y_val range (after inverse transform): min=0.0, max=2.0
No description has been provided for this image
-----------------------------------
Current item is  1465
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=94
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5106382978723404
x_eval_time shape before reshape: (1591, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1871, 14)
store_x_eval.shape: (208, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (170, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (165, 20)
Model: "sequential_128"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_257 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_162 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_258 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_163 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_127 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 111ms/step - loss: 1777.5757 - val_loss: 99.6648
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 305.4733 - val_loss: 0.5545
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 2.7107 - val_loss: 0.1199
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 1.1199 - val_loss: 0.0443
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.5669 - val_loss: 0.0173
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 0.3828 - val_loss: 0.1360
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.5657 - val_loss: 0.1505
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.3481 - val_loss: 0.0112
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.2822 - val_loss: 0.0094
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.2367 - val_loss: 0.0058
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1321 - val_loss: 0.0633
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1493 - val_loss: 0.0219
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1407 - val_loss: 0.0672
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1520 - val_loss: 0.0069
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1023 - val_loss: 0.0191
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0677 - val_loss: 0.0110
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 108ms/step - loss: 0.1329 - val_loss: 0.0109
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 108ms/step - loss: 0.0860 - val_loss: 0.0097
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0839 - val_loss: 0.3072
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1074 - val_loss: 0.0056
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0694 - val_loss: 0.0289
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0771 - val_loss: 0.0245
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0533 - val_loss: 0.1616
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0979 - val_loss: 0.0115
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0362 - val_loss: 0.0468
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0472 - val_loss: 0.4819
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1189 - val_loss: 0.0257
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 108ms/step - loss: 0.0321 - val_loss: 0.1822
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0733 - val_loss: 0.0054
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0745 - val_loss: 0.0091
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0334 - val_loss: 0.0685
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1372 - val_loss: 0.0337
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0386 - val_loss: 0.0098
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0320 - val_loss: 0.0078
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0240 - val_loss: 0.0047
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0174 - val_loss: 0.0077
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0738 - val_loss: 0.0185
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0551 - val_loss: 0.0043
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0166 - val_loss: 0.0295
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0237 - val_loss: 0.0232
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0547 - val_loss: 0.0044
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0213 - val_loss: 0.0120
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0288 - val_loss: 0.0047
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0205 - val_loss: 0.0044
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0132 - val_loss: 0.0047
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0207 - val_loss: 0.0125
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0333 - val_loss: 0.0044
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 108ms/step - loss: 0.0250 - val_loss: 0.0414
53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step 
Sample raw predictions (after inverse transform and clipping): [4.6931977 3.9251904 5.151284  2.185066  3.0338645]
RMSE =  7.6439342
Validation R-squared for item 1465: -0.011783719062805176
50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 28ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=37.372379302978516
True y_val range (after inverse transform): min=0.0, max=48.0
No description has been provided for this image
-----------------------------------
Current item is  1291
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=14
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6428571428571428
x_eval_time shape before reshape: (1659, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1939, 14)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (219, 20)
store_x_eval.shape: (181, 20)
Model: "sequential_129"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_259 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_164 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_260 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_165 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_128 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 112ms/step - loss: 16102.6553 - val_loss: 811.3441
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 1299.3628 - val_loss: 78.9109
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 193.3227 - val_loss: 49.1470
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 98.2203 - val_loss: 24.8354
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 56.0757 - val_loss: 10.2144
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 34.4090 - val_loss: 9.1613
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 20.9415 - val_loss: 4.4702
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 13.7581 - val_loss: 1.1526
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 9.2829 - val_loss: 1.9566
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 6.3890 - val_loss: 0.3736
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 4.6124 - val_loss: 0.7061
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 2.9069 - val_loss: 0.1715
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 1.8970 - val_loss: 0.3491
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 1.3501 - val_loss: 0.1987
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.9858 - val_loss: 0.0990
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.6534 - val_loss: 0.1171
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.6489 - val_loss: 0.0614
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.4813 - val_loss: 0.0449
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.5071 - val_loss: 0.1137
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.3763 - val_loss: 0.0224
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.2938 - val_loss: 0.0301
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2196 - val_loss: 0.0314
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2054 - val_loss: 0.1169
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.2438 - val_loss: 0.1766
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1914 - val_loss: 0.1210
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1486 - val_loss: 0.4602
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2205 - val_loss: 0.1158
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1742 - val_loss: 0.0127
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1574 - val_loss: 0.0915
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1574 - val_loss: 0.0125
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1207 - val_loss: 0.1246
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1836 - val_loss: 0.0347
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1266 - val_loss: 0.0128
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.1406 - val_loss: 0.0345
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.3270 - val_loss: 0.0662
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1801 - val_loss: 0.5065
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.3720 - val_loss: 0.0640
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1018 - val_loss: 0.0326
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.3387 - val_loss: 0.0108
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2062 - val_loss: 0.0128
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1500 - val_loss: 0.0094
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.4160 - val_loss: 0.2054
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2423 - val_loss: 0.2453
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1943 - val_loss: 1.2376
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.3358 - val_loss: 0.0751
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0959 - val_loss: 0.0093
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.4395 - val_loss: 0.1239
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0910 - val_loss: 0.0096
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0762 - val_loss: 0.0354
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.4144 - val_loss: 0.0601
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2033 - val_loss: 0.1264
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1208 - val_loss: 0.0239
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2875 - val_loss: 0.0564
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.3277 - val_loss: 0.0422
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.4116 - val_loss: 5.4515
Epoch 56/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 169.1634 - val_loss: 0.5198
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step 
Sample raw predictions (after inverse transform and clipping): [1.2624756 1.1463574 1.2206677 1.1041883 1.7262777]
RMSE =  1.372813
Validation R-squared for item 1291: -0.08289897441864014
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 29ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=3.798078775405884
True y_val range (after inverse transform): min=0.0, max=9.0
No description has been provided for this image
-----------------------------------
Current item is  1424
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=45
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.35555555555555557
x_eval_time shape before reshape: (1692, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1972, 14)
store_x_eval.shape: (213, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (210, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (176, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (195, 20)
Model: "sequential_130"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_261 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_166 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_262 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_167 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_129 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 112ms/step - loss: 16555.3027 - val_loss: 1055.9011
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 1088.1816 - val_loss: 114.7415
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 134.3547 - val_loss: 71.1422
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 78.3459 - val_loss: 42.7679
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 48.7246 - val_loss: 21.2582
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 32.2987 - val_loss: 15.8533
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 23.6187 - val_loss: 7.2489
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 16.4161 - val_loss: 6.7680
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 13.2771 - val_loss: 2.7323
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 10.8621 - val_loss: 4.1661
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 8.6918 - val_loss: 1.5530
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 7.0684 - val_loss: 0.4237
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 6.1815 - val_loss: 0.7660
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 4.4506 - val_loss: 0.3226
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 4.2689 - val_loss: 0.6778
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 3.2634 - val_loss: 0.5190
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 2.7713 - val_loss: 0.0771
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 2.0557 - val_loss: 0.0743
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 1.3968 - val_loss: 0.0710
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 0.9309 - val_loss: 0.0274
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.7333 - val_loss: 0.0603
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.7850 - val_loss: 0.0163
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.5288 - val_loss: 0.0265
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.5180 - val_loss: 0.0051
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.3653 - val_loss: 0.0093
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.3243 - val_loss: 0.0227
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 1.5620 - val_loss: 0.0168
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.2769 - val_loss: 0.0104
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.2287 - val_loss: 0.0409
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.2273 - val_loss: 0.0105
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.1986 - val_loss: 9.6365e-04
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.1576 - val_loss: 0.0119
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.1496 - val_loss: 0.0067
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.1849 - val_loss: 0.0024
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.1156 - val_loss: 0.0989
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.1101 - val_loss: 0.0152
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0565 - val_loss: 0.0326
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.0751 - val_loss: 0.0413
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.1574 - val_loss: 0.0028
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0779 - val_loss: 0.2170
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.1020 - val_loss: 0.0213
50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 33ms/step 
Sample raw predictions (after inverse transform and clipping): [1.2860903 1.7684357 1.3122737 1.6934654 1.9910855]
RMSE =  1.3479356
Validation R-squared for item 1424: -0.3858039379119873
53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 30ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=5.76594352722168
True y_val range (after inverse transform): min=0.0, max=16.0
No description has been provided for this image
-----------------------------------
Current item is  2504
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.0
x_eval_time shape before reshape: (1628, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1908, 14)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (151, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (167, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (222, 20)
Model: "sequential_131"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_263 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_168 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_264 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_169 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_130 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 13s 112ms/step - loss: 49501.1250 - val_loss: 1457.8612
Epoch 2/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 6897.5859 - val_loss: 291.0981
Epoch 3/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 1654.0198 - val_loss: 91.3291
Epoch 4/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 591.3803 - val_loss: 53.7344
Epoch 5/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 392.3601 - val_loss: 36.7696
Epoch 6/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 237.3869 - val_loss: 11.9364
Epoch 7/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 151.2194 - val_loss: 10.7370
Epoch 8/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 102.2039 - val_loss: 6.6637
Epoch 9/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 80.5547 - val_loss: 3.7511
Epoch 10/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 55.0267 - val_loss: 3.8237
Epoch 11/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 49.1561 - val_loss: 2.4406
Epoch 12/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 37.4136 - val_loss: 2.5521
Epoch 13/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 28.9135 - val_loss: 1.9241
Epoch 14/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 21.1801 - val_loss: 1.6021
Epoch 15/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 16.8246 - val_loss: 1.2353
Epoch 16/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 12.6793 - val_loss: 1.4521
Epoch 17/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 10.1088 - val_loss: 0.6198
Epoch 18/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 7.6257 - val_loss: 0.3926
Epoch 19/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 6.1579 - val_loss: 0.3628
Epoch 20/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 4.2015 - val_loss: 0.6605
Epoch 21/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 4.1416 - val_loss: 0.3572
Epoch 22/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 4.6031 - val_loss: 0.1419
Epoch 23/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 3.4177 - val_loss: 0.1585
Epoch 24/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 2.9606 - val_loss: 0.1034
Epoch 25/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 2.5789 - val_loss: 0.0516
Epoch 26/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 1.7069 - val_loss: 0.0883
Epoch 27/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 1.2539 - val_loss: 0.0733
Epoch 28/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 1.3308 - val_loss: 0.0928
Epoch 29/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 1.0886 - val_loss: 0.0744
Epoch 30/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.7453 - val_loss: 0.0213
Epoch 31/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 1.5673 - val_loss: 0.0191
Epoch 32/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 1.0726 - val_loss: 0.0086
Epoch 33/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 1.0207 - val_loss: 0.0876
Epoch 34/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.9975 - val_loss: 0.0467
Epoch 35/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 1.0030 - val_loss: 0.0372
Epoch 36/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 1.0663 - val_loss: 0.0130
Epoch 37/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.6083 - val_loss: 0.0068
Epoch 38/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.6745 - val_loss: 0.0155
Epoch 39/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.6257 - val_loss: 0.0070
Epoch 40/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.5369 - val_loss: 0.0155
Epoch 41/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.6417 - val_loss: 0.0076
Epoch 42/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.5153 - val_loss: 0.0069
Epoch 43/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.4003 - val_loss: 0.0243
Epoch 44/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.7704 - val_loss: 0.0148
Epoch 45/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 1.1722 - val_loss: 0.0138
Epoch 46/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.2443 - val_loss: 0.0164
Epoch 47/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.2777 - val_loss: 0.0026
Epoch 48/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.3870 - val_loss: 0.0061
Epoch 49/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2286 - val_loss: 0.0060
Epoch 50/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2514 - val_loss: 0.0066
Epoch 51/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1248 - val_loss: 0.0324
Epoch 52/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2122 - val_loss: 0.0109
Epoch 53/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1281 - val_loss: 0.0040
Epoch 54/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2736 - val_loss: 0.0026
Epoch 55/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2011 - val_loss: 0.0202
Epoch 56/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2423 - val_loss: 1.2271
Epoch 57/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.4234 - val_loss: 0.0437
50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step 
Sample raw predictions (after inverse transform and clipping): [0.         0.13709041 0.03325705 0.         0.0111235 ]
RMSE =  0.4554332
Validation R-squared for item 2504: -0.35741889476776123
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 29ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=7.019077301025391
True y_val range (after inverse transform): min=0.0, max=10.0
No description has been provided for this image
-----------------------------------
Current item is  2010
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.4
x_eval_time shape before reshape: (1589, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1869, 14)
store_x_eval.shape: (154, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (160, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (200, 20)
Model: "sequential_132"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_265 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_170 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_266 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_171 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_131 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 111ms/step - loss: 22493.8184 - val_loss: 2234.3696
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 4772.0278 - val_loss: 62.7961
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 290.8988 - val_loss: 30.0902
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 88.4296 - val_loss: 10.1840
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 38.5198 - val_loss: 2.4335
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 21.9122 - val_loss: 2.6231
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 13.1744 - val_loss: 1.1795
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 8.3515 - val_loss: 0.7782
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 6.0252 - val_loss: 2.7243
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 4.9337 - val_loss: 0.5583
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 2.0704 - val_loss: 0.2174
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.8485 - val_loss: 0.1420
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.8534 - val_loss: 0.0387
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.6515 - val_loss: 0.1992
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.7420 - val_loss: 0.0768
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.9445 - val_loss: 0.1881
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.4814 - val_loss: 0.0428
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.5088 - val_loss: 0.4363
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.4450 - val_loss: 0.1149
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.4149 - val_loss: 0.0155
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.2994 - val_loss: 0.1041
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.3306 - val_loss: 0.1352
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.2720 - val_loss: 0.0246
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.3184 - val_loss: 0.0354
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 1.1417 - val_loss: 0.0106
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.2126 - val_loss: 0.1318
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1487 - val_loss: 0.0758
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1600 - val_loss: 0.0938
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.2023 - val_loss: 0.0084
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1676 - val_loss: 0.0346
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1691 - val_loss: 0.0316
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1016 - val_loss: 0.1894
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1469 - val_loss: 0.0574
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.2326 - val_loss: 0.0911
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.7799 - val_loss: 0.0539
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1186 - val_loss: 0.0562
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0847 - val_loss: 0.0044
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0928 - val_loss: 0.0526
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0996 - val_loss: 0.0162
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0788 - val_loss: 0.0464
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1124 - val_loss: 0.0337
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0877 - val_loss: 0.1132
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1387 - val_loss: 0.0153
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1028 - val_loss: 0.0384
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0810 - val_loss: 0.0025
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0890 - val_loss: 0.0479
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1049 - val_loss: 0.0069
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1767 - val_loss: 0.0395
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0618 - val_loss: 0.0037
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0760 - val_loss: 0.0392
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0684 - val_loss: 0.0119
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0582 - val_loss: 0.1018
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 3.7670 - val_loss: 1.2228
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.6029 - val_loss: 0.0018
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0289 - val_loss: 0.0104
Epoch 56/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0276 - val_loss: 0.0015
Epoch 57/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0165 - val_loss: 0.0046
Epoch 58/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0373 - val_loss: 0.0120
Epoch 59/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0164 - val_loss: 0.0071
Epoch 60/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0394 - val_loss: 0.0048
Epoch 61/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0155 - val_loss: 0.0100
Epoch 62/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0162 - val_loss: 0.0026
Epoch 63/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0115 - val_loss: 0.0012
Epoch 64/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0144 - val_loss: 0.0083
Epoch 65/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0153 - val_loss: 0.0060
Epoch 66/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0402 - val_loss: 0.0026
Epoch 67/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0231 - val_loss: 0.0182
Epoch 68/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0238 - val_loss: 0.0013
Epoch 69/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1408 - val_loss: 0.1863
Epoch 70/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1251 - val_loss: 0.0053
Epoch 71/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0123 - val_loss: 0.0013
Epoch 72/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0112 - val_loss: 0.0049
Epoch 73/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0133 - val_loss: 0.0110
53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step 
Sample raw predictions (after inverse transform and clipping): [0.31749117 0.18959306 0.06241129 0.07039153 0.14451277]
RMSE =  0.39962238
Validation R-squared for item 2010: -0.12027716636657715
50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.7126240730285645
True y_val range (after inverse transform): min=0.0, max=4.0
No description has been provided for this image
-----------------------------------
Current item is  1445
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=27
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.37037037037037035
x_eval_time shape before reshape: (1673, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1953, 14)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (208, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (218, 20)
Model: "sequential_133"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_267 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_172 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_268 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_173 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_132 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 116ms/step - loss: 9759.8643 - val_loss: 42.2506
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 377.8294 - val_loss: 18.4006
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 113.0265 - val_loss: 5.9370
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 20.4474 - val_loss: 0.6966
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 5.0812 - val_loss: 0.2164
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 3.0266 - val_loss: 0.6688
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 2.6114 - val_loss: 0.4564
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 1.3599 - val_loss: 0.1441
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.9022 - val_loss: 0.0241
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.7821 - val_loss: 0.0576
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.7841 - val_loss: 0.4943
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.4081 - val_loss: 0.3008
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.5914 - val_loss: 0.0496
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.3676 - val_loss: 0.0085
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.2714 - val_loss: 0.2167
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.3167 - val_loss: 0.1324
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.2388 - val_loss: 0.3584
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.3686 - val_loss: 0.2015
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.1777 - val_loss: 0.0310
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1231 - val_loss: 0.0070
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.6323 - val_loss: 4.9485
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1.4239 - val_loss: 0.0064
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0797 - val_loss: 0.0094
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.0627 - val_loss: 0.0055
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0611 - val_loss: 0.0070
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0467 - val_loss: 0.0054
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.0686 - val_loss: 0.0082
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.0390 - val_loss: 0.0052
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.0470 - val_loss: 0.0169
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.3901 - val_loss: 0.0055
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0224 - val_loss: 0.0128
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.0367 - val_loss: 0.0865
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.0505 - val_loss: 0.0326
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.0677 - val_loss: 0.0593
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.0616 - val_loss: 0.0133
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2500 - val_loss: 0.0052
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1632 - val_loss: 0.0135
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0589 - val_loss: 0.0121
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 34ms/step 
Sample raw predictions (after inverse transform and clipping): [1.78407   1.6539694 1.648487  1.751756  1.7345482]
RMSE =  1.8927971
Validation R-squared for item 1445: -0.057062625885009766
53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=2.931734561920166
True y_val range (after inverse transform): min=0.0, max=10.0
No description has been provided for this image
-----------------------------------
Current item is  2012
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=9
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6666666666666666
x_eval_time shape before reshape: (1627, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1907, 14)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (209, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (203, 20)
Model: "sequential_134"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_269 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_174 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_270 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_175 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_133 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 116ms/step - loss: 15997.5273 - val_loss: 2057.2039
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 2488.5540 - val_loss: 315.2719
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 788.5375 - val_loss: 24.2098
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 47.0511 - val_loss: 14.1125
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 26.5127 - val_loss: 10.4657
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 17.3912 - val_loss: 2.6465
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 10.8316 - val_loss: 1.3057
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 7.3124 - val_loss: 2.7242
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 5.7847 - val_loss: 0.1239
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 4.8142 - val_loss: 0.1816
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 4.3286 - val_loss: 0.3363
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 3.5529 - val_loss: 0.1333
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 2.7549 - val_loss: 0.1304
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 2.2311 - val_loss: 0.7254
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 1.8125 - val_loss: 0.2678
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 1.4099 - val_loss: 0.1958
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 1.4868 - val_loss: 0.3266
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 1.4142 - val_loss: 0.8220
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 1.1983 - val_loss: 0.0364
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 1.1097 - val_loss: 0.6364
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.8681 - val_loss: 0.2359
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 0.8915 - val_loss: 0.1001
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 0.8201 - val_loss: 0.4480
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 0.7024 - val_loss: 0.0295
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 0.6076 - val_loss: 0.0335
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.6390 - val_loss: 0.2120
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.4247 - val_loss: 0.0486
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.3498 - val_loss: 0.5048
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 0.4375 - val_loss: 0.0752
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.5466 - val_loss: 0.0742
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.4307 - val_loss: 1.0079
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.5522 - val_loss: 0.0088
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 0.2648 - val_loss: 0.0374
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 0.2907 - val_loss: 0.0071
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.7496 - val_loss: 0.0560
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.3639 - val_loss: 1.6129
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.4689 - val_loss: 0.0187
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2262 - val_loss: 0.0095
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 0.4785 - val_loss: 0.2133
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.4165 - val_loss: 0.0789
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2578 - val_loss: 0.2049
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2013 - val_loss: 0.0300
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1782 - val_loss: 0.2844
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2657 - val_loss: 0.0575
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 34ms/step 
Sample raw predictions (after inverse transform and clipping): [0.4251839  0.49944162 1.1400505  0.93561727 0.        ]
RMSE =  0.81538653
Validation R-squared for item 2012: -0.2776836156845093
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.5421051979064941
True y_val range (after inverse transform): min=0.0, max=6.0
No description has been provided for this image
-----------------------------------
Current item is  2168
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5
x_eval_time shape before reshape: (1632, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1912, 14)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (213, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (185, 20)
Model: "sequential_135"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_271 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_176 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_272 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_177 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_134 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 118ms/step - loss: 34375.7148 - val_loss: 57160.7188
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 41967.8672 - val_loss: 438.1059
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 3281.5559 - val_loss: 310.9775
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 867.9604 - val_loss: 36.3859
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 343.6973 - val_loss: 40.3279
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 178.7780 - val_loss: 34.3111
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 293.1850 - val_loss: 35.2279
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 296.8963 - val_loss: 18.2245
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 167.3350 - val_loss: 10.5285
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 125.2502 - val_loss: 7.7910
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 80.4312 - val_loss: 2.7357
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 41.4211 - val_loss: 0.4691
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 25.4672 - val_loss: 1.0255
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 23.7747 - val_loss: 1.4716
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 15.4867 - val_loss: 1.5887
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 16.2243 - val_loss: 0.7170
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 9.0034 - val_loss: 3.1795
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 7.2614 - val_loss: 1.8916
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 5.9429 - val_loss: 0.1366
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 6.6018 - val_loss: 4.4103
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 5.8435 - val_loss: 20.4598
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 18.1962 - val_loss: 0.6939
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 2.2026 - val_loss: 0.0686
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 2.6305 - val_loss: 0.2914
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 2.0530 - val_loss: 0.0637
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.6149 - val_loss: 0.0192
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.6225 - val_loss: 1.9252
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1.6975 - val_loss: 0.6123
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.6671 - val_loss: 0.5671
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 4.4208 - val_loss: 1.1488
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 2.6565 - val_loss: 0.6577
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.2814 - val_loss: 0.2639
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1.6668 - val_loss: 0.0595
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 2.3514 - val_loss: 0.0309
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.8982 - val_loss: 0.2243
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 2.0716 - val_loss: 0.2693
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 33ms/step 
Sample raw predictions (after inverse transform and clipping): [0.         0.         0.         0.49052805 0.78078806]
RMSE =  1.1071262
Validation R-squared for item 2168: -6.757527828216553
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=11.21563720703125
True y_val range (after inverse transform): min=0.0, max=5.0
No description has been provided for this image
-----------------------------------
Current item is  1050
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=23
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6956521739130435
x_eval_time shape before reshape: (1643, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1923, 14)
store_x_eval.shape: (168, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (212, 20)
Model: "sequential_136"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_273 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_178 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_274 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_179 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_135 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 117ms/step - loss: 25401.0293 - val_loss: 5332.6982
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 7934.6445 - val_loss: 215.2274
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 841.3720 - val_loss: 54.8551
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 351.6442 - val_loss: 27.3799
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 199.8824 - val_loss: 14.0569
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 126.3875 - val_loss: 10.4290
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 87.8577 - val_loss: 7.3625
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 72.2663 - val_loss: 8.3979
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 53.3199 - val_loss: 5.0769
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 45.1260 - val_loss: 4.8873
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 33.2359 - val_loss: 3.4527
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 27.6061 - val_loss: 1.3221
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 23.9589 - val_loss: 1.7102
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 19.6147 - val_loss: 1.7267
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 19.1600 - val_loss: 1.2711
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 14.2480 - val_loss: 0.6413
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 12.4750 - val_loss: 0.8892
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 14.3111 - val_loss: 0.4509
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 8.3742 - val_loss: 0.5119
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 11.2719 - val_loss: 0.6389
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 7.2885 - val_loss: 0.5851
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 5.8204 - val_loss: 1.6331
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 4.8384 - val_loss: 0.4829
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 5.8830 - val_loss: 1.3201
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 4.2526 - val_loss: 0.0726
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 4.8251 - val_loss: 1.1619
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 4.4220 - val_loss: 0.2517
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 3.8236 - val_loss: 0.2093
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 2.9882 - val_loss: 0.4681
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 2.6417 - val_loss: 0.2830
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 2.3610 - val_loss: 0.1956
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 3.2775 - val_loss: 0.9988
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1.7297 - val_loss: 0.5010
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 2.3525 - val_loss: 0.4577
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1.7956 - val_loss: 4.2418
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 35ms/step 
Sample raw predictions (after inverse transform and clipping): [ 6.332184    0.16677544 13.066532    9.544417    0.        ]
RMSE =  4.7892513
Validation R-squared for item 1050: -3.829493999481201
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=21.054485321044922
True y_val range (after inverse transform): min=0.0, max=16.0
No description has been provided for this image
-----------------------------------
Current item is  1177
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=30
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6
x_eval_time shape before reshape: (1623, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1903, 14)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (170, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (166, 20)
Model: "sequential_137"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_275 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_180 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_276 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_181 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_136 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 117ms/step - loss: 44624.7773 - val_loss: 2902.3999
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 10833.3096 - val_loss: 703.4358
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 2914.1763 - val_loss: 324.6014
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1799.7914 - val_loss: 170.4707
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1145.2549 - val_loss: 154.1617
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 747.6458 - val_loss: 162.2749
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 553.6264 - val_loss: 120.6299
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 408.1453 - val_loss: 91.0298
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 318.3636 - val_loss: 48.3396
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 195.3276 - val_loss: 25.2634
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 111.6279 - val_loss: 11.2847
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 60.7731 - val_loss: 4.2480
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 28.6335 - val_loss: 1.2984
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 27.4946 - val_loss: 2.2459
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 19.7037 - val_loss: 1.3870
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 13.1685 - val_loss: 1.3600
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 9.4184 - val_loss: 0.7113
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 6.7274 - val_loss: 0.5823
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 5.6803 - val_loss: 0.6020
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 3.6509 - val_loss: 0.4290
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 3.2265 - val_loss: 0.2049
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 2.1720 - val_loss: 0.2734
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 2.0048 - val_loss: 0.1276
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1.5857 - val_loss: 0.0609
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.3097 - val_loss: 0.0525
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 1.0153 - val_loss: 0.0848
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.9369 - val_loss: 0.0807
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.9297 - val_loss: 0.0257
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.7633 - val_loss: 0.0441
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.8792 - val_loss: 0.0930
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.6076 - val_loss: 0.0291
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.5268 - val_loss: 0.0247
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.4986 - val_loss: 0.0119
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.4578 - val_loss: 0.0114
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.4029 - val_loss: 0.1864
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.5384 - val_loss: 0.0157
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.3586 - val_loss: 0.0266
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.3895 - val_loss: 0.0467
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.2870 - val_loss: 0.0126
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2704 - val_loss: 0.1298
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.2669 - val_loss: 0.0110
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.2576 - val_loss: 0.0099
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2595 - val_loss: 0.0587
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.2122 - val_loss: 0.0054
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2208 - val_loss: 0.0052
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2342 - val_loss: 0.0228
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2380 - val_loss: 0.0055
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2208 - val_loss: 0.0084
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2027 - val_loss: 0.0057
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1823 - val_loss: 0.0115
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.3050 - val_loss: 0.0474
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1982 - val_loss: 0.0707
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.2141 - val_loss: 0.0204
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1474 - val_loss: 0.0135
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1662 - val_loss: 0.0607
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 33ms/step 
Sample raw predictions (after inverse transform and clipping): [1.0407993 1.7573586 1.4073275 1.1716727 1.2933131]
RMSE =  2.8082244
Validation R-squared for item 1177: -0.34910428524017334
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=11.239595413208008
True y_val range (after inverse transform): min=0.0, max=18.0
No description has been provided for this image
-----------------------------------
Current item is  2156
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=6
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6666666666666666
x_eval_time shape before reshape: (1625, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1905, 14)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (172, 20)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (177, 20)
Model: "sequential_138"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_277 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_182 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_278 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_183 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_137 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 117ms/step - loss: 8290.8770 - val_loss: 111.9026
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 338.8893 - val_loss: 5.1481
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 61.8050 - val_loss: 2.0055
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 10.5168 - val_loss: 0.1029
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1.3678 - val_loss: 0.1296
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.6785 - val_loss: 0.0467
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.3826 - val_loss: 0.3447
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2585 - val_loss: 0.0316
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.2597 - val_loss: 0.8128
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.3207 - val_loss: 0.0954
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.1307 - val_loss: 0.0302
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1812 - val_loss: 0.7124
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2483 - val_loss: 0.0239
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0689 - val_loss: 0.0073
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1533 - val_loss: 0.0497
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2488 - val_loss: 0.0305
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1323 - val_loss: 0.1712
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0943 - val_loss: 0.0080
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2884 - val_loss: 0.0375
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0428 - val_loss: 0.0261
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0413 - val_loss: 0.0146
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0307 - val_loss: 0.0494
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.5782 - val_loss: 0.0043
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.0226 - val_loss: 0.0089
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.0738 - val_loss: 0.0483
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.0444 - val_loss: 0.0798
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0571 - val_loss: 0.0052
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0633 - val_loss: 0.0446
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.0980 - val_loss: 0.0101
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.0292 - val_loss: 0.0216
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0782 - val_loss: 0.0110
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0816 - val_loss: 0.0627
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.0462 - val_loss: 1.2285
54/54 ━━━━━━━━━━━━━━━━━━━━ 2s 36ms/step 
Sample raw predictions (after inverse transform and clipping): [0.02438923 0.01139406 0.04834878 0.06852264 0.01114932]
RMSE =  0.49918652
Validation R-squared for item 2156: -0.08563041687011719
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.5729254484176636
True y_val range (after inverse transform): min=0.0, max=4.0
No description has been provided for this image
-----------------------------------
Current item is  1361
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=24
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6666666666666666
x_eval_time shape before reshape: (1601, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1881, 14)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (174, 20)
Model: "sequential_139"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_279 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_184 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_280 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_185 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_138 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 13s 112ms/step - loss: 29882.0352 - val_loss: 119667.7656
Epoch 2/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 97016.4297 - val_loss: 119.2520
Epoch 3/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 1940.2581 - val_loss: 52.8342
Epoch 4/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 723.1902 - val_loss: 36.5917
Epoch 5/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 402.7668 - val_loss: 25.5489
Epoch 6/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 290.3178 - val_loss: 19.2731
Epoch 7/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 213.9572 - val_loss: 20.1421
Epoch 8/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 185.3600 - val_loss: 12.2804
Epoch 9/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 144.3360 - val_loss: 15.3426
Epoch 10/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 118.7076 - val_loss: 41.7046
Epoch 11/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 105.7787 - val_loss: 8.8359
Epoch 12/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 91.2208 - val_loss: 8.3054
Epoch 13/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 80.5774 - val_loss: 10.6796
Epoch 14/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 67.5216 - val_loss: 6.6743
Epoch 15/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 58.4710 - val_loss: 13.1262
Epoch 16/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 52.7549 - val_loss: 6.0644
Epoch 17/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 46.0605 - val_loss: 7.7990
Epoch 18/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 43.0764 - val_loss: 13.4443
Epoch 19/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 38.4902 - val_loss: 4.1586
Epoch 20/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 32.7660 - val_loss: 31.9534
Epoch 21/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 34.0813 - val_loss: 13.5719
Epoch 22/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 29.7181 - val_loss: 6.1791
Epoch 23/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 27.2596 - val_loss: 3.5494
Epoch 24/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 22.1809 - val_loss: 2.8846
Epoch 25/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 20.1342 - val_loss: 10.6376
Epoch 26/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 21.1633 - val_loss: 4.1040
Epoch 27/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 14.9510 - val_loss: 2.6717
Epoch 28/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 15.0132 - val_loss: 1.6112
Epoch 29/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 14.1537 - val_loss: 1.5618
Epoch 30/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 11.2768 - val_loss: 5.3655
Epoch 31/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 10.8906 - val_loss: 3.1102
Epoch 32/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 10.0422 - val_loss: 1.8317
Epoch 33/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 9.6504 - val_loss: 2.2949
Epoch 34/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 8.8335 - val_loss: 0.7166
Epoch 35/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 7.6829 - val_loss: 1.1615
Epoch 36/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 7.4833 - val_loss: 3.4228
Epoch 37/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 6.7174 - val_loss: 7.7666
Epoch 38/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 6.6711 - val_loss: 0.8106
Epoch 39/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 5.9581 - val_loss: 28.3848
Epoch 40/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 10.6577 - val_loss: 1.1211
Epoch 41/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 4.6302 - val_loss: 12.6691
Epoch 42/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 8.2593 - val_loss: 1.5008
Epoch 43/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 4.6629 - val_loss: 4.0521
Epoch 44/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 4.3626 - val_loss: 4.1796
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 33ms/step 
Sample raw predictions (after inverse transform and clipping): [0.       0.       0.       0.       9.498136]
RMSE =  12.133267
Validation R-squared for item 1361: -26.97688102722168
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 30ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=53.450992584228516
True y_val range (after inverse transform): min=0.0, max=16.0
No description has been provided for this image
-----------------------------------
Current item is  1074
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=18
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.4444444444444444
x_eval_time shape before reshape: (1618, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1898, 14)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (204, 20)
Model: "sequential_140"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_281 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_186 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_282 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_187 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_139 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 118ms/step - loss: 20917.5977 - val_loss: 523.7988
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 1820.9797 - val_loss: 77.7954
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 469.9118 - val_loss: 52.5772
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 193.7009 - val_loss: 10.2752
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 81.8956 - val_loss: 1.9827
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 39.7989 - val_loss: 2.4639
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 26.8196 - val_loss: 1.3595
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 15.0658 - val_loss: 0.3992
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 13.4954 - val_loss: 0.8631
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 10.6634 - val_loss: 0.6887
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 8.6248 - val_loss: 1.0273
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 7.2048 - val_loss: 0.1866
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 6.0716 - val_loss: 0.1143
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 4.3828 - val_loss: 0.0894
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 2.6461 - val_loss: 0.5092
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 2.9192 - val_loss: 0.5139
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 2.6219 - val_loss: 0.1771
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.5052 - val_loss: 0.1125
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.3561 - val_loss: 0.2117
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 1.1969 - val_loss: 0.0669
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.8832 - val_loss: 0.3111
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 0.8437 - val_loss: 0.0501
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.6223 - val_loss: 0.1746
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.5145 - val_loss: 0.0632
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.6009 - val_loss: 0.0918
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.5355 - val_loss: 0.0360
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.4732 - val_loss: 0.1401
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.4697 - val_loss: 0.0497
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.4169 - val_loss: 0.0670
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.3780 - val_loss: 0.0359
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 0.3514 - val_loss: 0.0534
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.3660 - val_loss: 0.0365
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 0.3136 - val_loss: 0.0169
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 0.2680 - val_loss: 0.0544
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 0.2176 - val_loss: 0.0372
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 0.2073 - val_loss: 0.7319
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 0.4145 - val_loss: 0.2465
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.2529 - val_loss: 0.0893
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 0.1809 - val_loss: 0.0397
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1757 - val_loss: 0.0340
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.3816 - val_loss: 0.0143
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1947 - val_loss: 0.0664
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.4427 - val_loss: 0.0350
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1479 - val_loss: 0.1033
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.2252 - val_loss: 0.0262
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.1352 - val_loss: 0.2111
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1811 - val_loss: 0.0065
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1827 - val_loss: 0.0470
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.1285 - val_loss: 0.0238
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.1484 - val_loss: 0.0202
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.1716 - val_loss: 0.0255
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.2319 - val_loss: 0.0648
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.2002 - val_loss: 0.0153
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.4320 - val_loss: 0.1047
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.4890 - val_loss: 0.1293
Epoch 56/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.5835 - val_loss: 0.1843
Epoch 57/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1540 - val_loss: 0.0073
54/54 ━━━━━━━━━━━━━━━━━━━━ 2s 36ms/step 
Sample raw predictions (after inverse transform and clipping): [1.187185  1.1976706 1.1143035 0.7609577 0.       ]
RMSE =  1.288355
Validation R-squared for item 1074: -0.9366446733474731
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 33ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=7.25835657119751
True y_val range (after inverse transform): min=0.0, max=8.0
No description has been provided for this image
-----------------------------------
Current item is  1265
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=12
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6666666666666666
x_eval_time shape before reshape: (1656, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1936, 14)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (167, 20)
Model: "sequential_141"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_283 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_188 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_284 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_189 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_140 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 120ms/step - loss: 8322.6650 - val_loss: 116.4030
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 316.2222 - val_loss: 12.1824
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 83.1694 - val_loss: 2.9947
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 34.6315 - val_loss: 2.9784
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 17.8371 - val_loss: 0.6925
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 6.7460 - val_loss: 0.6268
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 6.0347 - val_loss: 2.5081
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 7.0590 - val_loss: 0.2629
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 3.4114 - val_loss: 0.1374
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 1.7789 - val_loss: 0.6894
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 1.3210 - val_loss: 0.1150
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 1.2440 - val_loss: 0.4577
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.8917 - val_loss: 0.0819
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.6527 - val_loss: 0.0689
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.5739 - val_loss: 0.0395
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.4467 - val_loss: 0.0326
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.4690 - val_loss: 0.0504
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.3381 - val_loss: 2.1390
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.6816 - val_loss: 0.8637
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.6758 - val_loss: 0.4268
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.4208 - val_loss: 0.6666
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.4116 - val_loss: 0.1016
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.3209 - val_loss: 0.0293
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.2099 - val_loss: 0.0552
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.4587 - val_loss: 0.5189
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.2738 - val_loss: 0.0280
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.2168 - val_loss: 0.0343
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.2138 - val_loss: 0.1443
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.4095 - val_loss: 0.0119
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1434 - val_loss: 0.0508
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.2106 - val_loss: 0.1274
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.3286 - val_loss: 0.0400
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.2626 - val_loss: 0.0208
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.1257 - val_loss: 0.0119
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.2948 - val_loss: 0.0117
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 0.1368 - val_loss: 0.0915
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.3200 - val_loss: 0.0287
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.1398 - val_loss: 0.0318
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.1152 - val_loss: 0.0227
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.3421 - val_loss: 0.0348
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.2457 - val_loss: 0.0242
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.1664 - val_loss: 0.0225
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.3754 - val_loss: 0.0135
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.2271 - val_loss: 0.0262
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1194 - val_loss: 0.0921
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 39ms/step 
Sample raw predictions (after inverse transform and clipping): [0.7510407  0.7246634  0.77833104 0.8691641  1.2433431 ]
RMSE =  1.1674118
Validation R-squared for item 1265: -0.31642043590545654
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 33ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=7.536468029022217
True y_val range (after inverse transform): min=0.0, max=8.0
No description has been provided for this image
-----------------------------------
Current item is  2221
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=14
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8571428571428571
x_eval_time shape before reshape: (1665, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1945, 14)
store_x_eval.shape: (171, 20)
store_x_eval.shape: (219, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (199, 20)
Model: "sequential_142"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_285 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_190 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_286 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_191 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_141 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 119ms/step - loss: 35925.5117 - val_loss: 1566.6349
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 2116.3655 - val_loss: 81.7741
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 430.7963 - val_loss: 136.3681
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 211.5713 - val_loss: 35.1650
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 118.7193 - val_loss: 23.1144
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 81.8371 - val_loss: 7.9310
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 57.1146 - val_loss: 7.6643
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 41.7110 - val_loss: 3.7283
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 32.5754 - val_loss: 3.0487
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 27.1959 - val_loss: 8.0489
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 23.3065 - val_loss: 2.1000
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 18.7051 - val_loss: 3.0002
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 15.3172 - val_loss: 7.1859
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 14.5911 - val_loss: 30.0302
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 16.8607 - val_loss: 2.2638
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 9.5578 - val_loss: 1.1437
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 7.9501 - val_loss: 1.1942
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 7.5098 - val_loss: 6.8789
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 8.8689 - val_loss: 2.8745
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 6.8338 - val_loss: 1.7127
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 5.2079 - val_loss: 0.3755
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 4.0529 - val_loss: 0.6279
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 3.6876 - val_loss: 0.8808
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 3.3246 - val_loss: 8.1875
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 4.5008 - val_loss: 5.7519
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 3.9211 - val_loss: 1.1800
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 2.2923 - val_loss: 1.8609
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 2.2766 - val_loss: 2.6087
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 2.1976 - val_loss: 1.4468
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 1.8459 - val_loss: 1.3376
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 1.4632 - val_loss: 1.9081
53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 35ms/step 
Sample raw predictions (after inverse transform and clipping): [1.9744182 0.        0.        0.        0.       ]
RMSE =  6.187594
Validation R-squared for item 2221: -96.90941619873047
53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=27.673616409301758
True y_val range (after inverse transform): min=0.0, max=12.0
No description has been provided for this image
-----------------------------------
Current item is  1164
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=14
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6428571428571428
x_eval_time shape before reshape: (1639, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1919, 14)
store_x_eval.shape: (216, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (183, 20)
Model: "sequential_143"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_287 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_192 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_288 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_193 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_142 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 115ms/step - loss: 11416.3867 - val_loss: 231.5361
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 1104.9559 - val_loss: 41.3923
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 177.8463 - val_loss: 17.2359
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 76.4523 - val_loss: 5.7385
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 38.7141 - val_loss: 3.5160
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 25.2371 - val_loss: 3.1355
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 16.6843 - val_loss: 6.0632
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 11.6400 - val_loss: 2.5194
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 6.7764 - val_loss: 1.4476
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 5.9609 - val_loss: 0.5749
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 3.3708 - val_loss: 2.2133
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 2.0049 - val_loss: 0.9450
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 1.2472 - val_loss: 0.3053
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 1.0738 - val_loss: 0.2450
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.9013 - val_loss: 2.0997
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.8767 - val_loss: 3.9912
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 1.1002 - val_loss: 0.0164
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.5083 - val_loss: 0.0855
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.3688 - val_loss: 0.0321
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.4162 - val_loss: 0.0231
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.4138 - val_loss: 0.3173
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.8374 - val_loss: 0.0505
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.5147 - val_loss: 0.0074
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.4933 - val_loss: 1.3405
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 1.1239 - val_loss: 0.0631
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2173 - val_loss: 0.3794
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.3338 - val_loss: 0.0202
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.1948 - val_loss: 1.0111
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 1.0570 - val_loss: 1.2107
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.7285 - val_loss: 0.3282
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.6428 - val_loss: 0.0262
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.1974 - val_loss: 0.0265
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 5.8214 - val_loss: 0.1156
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 35ms/step 
Sample raw predictions (after inverse transform and clipping): [0.08827338 0.         0.5581268  0.         0.04601315]
RMSE =  1.1990379
Validation R-squared for item 1164: -0.08762538433074951
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.6346347332000732
True y_val range (after inverse transform): min=0.0, max=9.0
No description has been provided for this image
-----------------------------------
Current item is  2106
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=6
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6666666666666666
x_eval_time shape before reshape: (1644, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1924, 14)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (210, 20)
store_x_eval.shape: (176, 20)
Model: "sequential_144"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_289 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_194 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_290 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_195 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_143 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 120ms/step - loss: 13085.9102 - val_loss: 315.2415
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 754.3457 - val_loss: 37.7731
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 170.2674 - val_loss: 30.6020
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 69.8272 - val_loss: 3.0887
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 30.7674 - val_loss: 1.3729
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 15.6875 - val_loss: 1.6926
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 9.5343 - val_loss: 2.3414
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 7.0649 - val_loss: 0.8414
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 4.9869 - val_loss: 0.3043
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 3.7756 - val_loss: 0.2602
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 2.4585 - val_loss: 0.1127
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 2.0239 - val_loss: 0.8605
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 1.5819 - val_loss: 0.0411
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 1.4185 - val_loss: 0.0205
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 1.1651 - val_loss: 0.9578
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 1.0397 - val_loss: 1.2689
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 1.1777 - val_loss: 0.0219
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 1.2012 - val_loss: 0.0575
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.6238 - val_loss: 0.0101
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.7637 - val_loss: 0.1181
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.6825 - val_loss: 0.5891
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.6575 - val_loss: 0.0536
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.5505 - val_loss: 0.4619
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.6440 - val_loss: 0.3514
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.4850 - val_loss: 1.8509
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 0.8169 - val_loss: 0.0098
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.3760 - val_loss: 0.0681
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.3862 - val_loss: 0.0446
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.4202 - val_loss: 0.1380
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.3742 - val_loss: 0.0890
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.2327 - val_loss: 0.0044
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.2420 - val_loss: 0.0302
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.3971 - val_loss: 0.0085
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.2045 - val_loss: 0.0392
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.4587 - val_loss: 0.0046
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.3240 - val_loss: 0.0864
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.2062 - val_loss: 0.0659
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.2345 - val_loss: 0.0192
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.1717 - val_loss: 0.3709
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.2253 - val_loss: 0.0057
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.2487 - val_loss: 0.0861
53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 35ms/step 
Sample raw predictions (after inverse transform and clipping): [0.         0.13146748 0.08906481 0.16355906 0.26161784]
RMSE =  0.40588656
Validation R-squared for item 2106: -0.3254774808883667
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.9727151393890381
True y_val range (after inverse transform): min=0.0, max=4.0
No description has been provided for this image
-----------------------------------
Current item is  2199
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=16
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.75
x_eval_time shape before reshape: (1687, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1967, 14)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (209, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (215, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (179, 20)
Model: "sequential_145"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_291 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_196 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_292 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_197 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_144 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 123ms/step - loss: 8127.8408 - val_loss: 680.4302
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 592.5879 - val_loss: 26.4985
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 78.3791 - val_loss: 4.8960
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 22.6474 - val_loss: 0.5114
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 3.1195 - val_loss: 0.2576
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 1.3928 - val_loss: 0.0700
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.8203 - val_loss: 0.0289
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.4228 - val_loss: 0.0128
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.2294 - val_loss: 0.0178
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.1942 - val_loss: 0.0139
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1572 - val_loss: 0.0056
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.1184 - val_loss: 0.0124
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0911 - val_loss: 0.0071
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1161 - val_loss: 0.0091
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0981 - val_loss: 0.0092
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0531 - val_loss: 0.0119
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.0599 - val_loss: 0.0043
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0555 - val_loss: 0.0067
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0608 - val_loss: 0.0099
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.0232 - val_loss: 0.0049
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0243 - val_loss: 0.0040
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0153 - val_loss: 0.0045
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0195 - val_loss: 0.0033
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0194 - val_loss: 0.0032
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0147 - val_loss: 0.0124
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.0257 - val_loss: 0.0039
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.0134 - val_loss: 0.0036
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0271 - val_loss: 0.0033
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.0139 - val_loss: 0.0046
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.0148 - val_loss: 0.0037
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0129 - val_loss: 0.0098
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.0175 - val_loss: 0.0321
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0234 - val_loss: 0.0142
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0226 - val_loss: 0.0052
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 37ms/step 
Sample raw predictions (after inverse transform and clipping): [0.65381753 0.6351657  0.5634749  0.59005505 0.6541003 ]
RMSE =  1.1718154
Validation R-squared for item 2199: -0.02255237102508545
53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 33ms/step
Predicted y_val range (after inverse transform and clipping): min=0.3950556814670563, max=1.8669464588165283
True y_val range (after inverse transform): min=0.0, max=12.0
No description has been provided for this image
-----------------------------------
Current item is  1504
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=12
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5833333333333333
x_eval_time shape before reshape: (1636, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1916, 14)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (219, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (193, 20)
Model: "sequential_146"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_293 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_198 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_294 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_199 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_145 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 123ms/step - loss: 27361.2969 - val_loss: 856.7946
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 2915.4482 - val_loss: 258.5603
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 872.1925 - val_loss: 80.1317
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 383.4228 - val_loss: 68.7992
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 291.5343 - val_loss: 23.5859
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 163.3622 - val_loss: 16.9654
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 105.6384 - val_loss: 10.8264
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 67.3603 - val_loss: 5.3923
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 49.4094 - val_loss: 4.2467
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 32.3661 - val_loss: 4.8813
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 28.7909 - val_loss: 1.6952
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 20.0658 - val_loss: 2.3409
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 14.8445 - val_loss: 0.9293
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 10.2542 - val_loss: 0.5404
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 8.8571 - val_loss: 0.3689
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 6.2965 - val_loss: 0.3785
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 5.3717 - val_loss: 0.3697
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 3.6498 - val_loss: 0.5677
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 4.1552 - val_loss: 0.4239
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 3.1401 - val_loss: 0.2402
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 2.4635 - val_loss: 0.3073
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 1.7773 - val_loss: 0.0999
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 1.4679 - val_loss: 0.3469
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 1.6180 - val_loss: 0.1112
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 1.0323 - val_loss: 0.1014
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 1.0847 - val_loss: 0.1046
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.7567 - val_loss: 0.0410
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.7328 - val_loss: 0.3103
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.5946 - val_loss: 0.0905
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.4114 - val_loss: 0.1110
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.3836 - val_loss: 0.1558
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.4129 - val_loss: 0.0133
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.4139 - val_loss: 0.0355
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.2908 - val_loss: 0.2765
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.3250 - val_loss: 0.0153
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.2860 - val_loss: 0.0246
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.2625 - val_loss: 0.0889
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.2474 - val_loss: 0.0124
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.1995 - val_loss: 0.0312
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.2089 - val_loss: 0.1006
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.2553 - val_loss: 0.0941
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.2001 - val_loss: 0.2058
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.1543 - val_loss: 0.0185
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.0926 - val_loss: 0.0092
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.0923 - val_loss: 0.1669
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.2628 - val_loss: 0.0314
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.1502 - val_loss: 0.1474
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.2291 - val_loss: 0.0129
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.1486 - val_loss: 0.0164
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.0691 - val_loss: 0.0339
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.0772 - val_loss: 0.0177
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.1521 - val_loss: 0.0114
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.0529 - val_loss: 0.0179
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.1941 - val_loss: 0.7078
53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 37ms/step 
Sample raw predictions (after inverse transform and clipping): [0.7461023  0.48899382 0.37014005 0.8506138  0.88745767]
RMSE =  0.9063988
Validation R-squared for item 1504: -0.39566922187805176
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 34ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=7.653149604797363
True y_val range (after inverse transform): min=0.0, max=7.0
No description has been provided for this image
-----------------------------------
Current item is  1217
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=17
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7058823529411764
x_eval_time shape before reshape: (1652, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1932, 14)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (168, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (207, 20)
Model: "sequential_147"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_295 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_200 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_296 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_201 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_146 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 117ms/step - loss: 26720.2051 - val_loss: 3780.3308
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 10097.9482 - val_loss: 90.9688
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 573.0079 - val_loss: 35.5644
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 184.0097 - val_loss: 15.6040
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 91.8896 - val_loss: 4.3280
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 23.9071 - val_loss: 1.6444
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 13.0078 - val_loss: 4.6116
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 4.8410 - val_loss: 0.3697
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 2.7283 - val_loss: 0.1159
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.7987 - val_loss: 0.1589
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 3.2258 - val_loss: 1.3163
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 4.1560 - val_loss: 0.1219
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 2.8311 - val_loss: 1.0277
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 2.3933 - val_loss: 0.4552
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.8272 - val_loss: 0.0424
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.4311 - val_loss: 0.0550
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.1812 - val_loss: 0.2172
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1.1214 - val_loss: 0.2580
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1.0720 - val_loss: 0.1364
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 2.2061 - val_loss: 0.0444
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 0.7031 - val_loss: 0.3191
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 0.7277 - val_loss: 0.2744
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1.3403 - val_loss: 1.0376
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.3888 - val_loss: 0.3477
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 0.8899 - val_loss: 0.2706
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 36ms/step 
Sample raw predictions (after inverse transform and clipping): [0.        0.        0.        1.4293045 0.       ]
RMSE =  1.779017
Validation R-squared for item 1217: -0.494115948677063
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 33ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=15.705046653747559
True y_val range (after inverse transform): min=0.0, max=12.0
No description has been provided for this image
-----------------------------------
Current item is  2416
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=16
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.625
x_eval_time shape before reshape: (1675, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1955, 14)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (230, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (173, 20)
store_x_eval.shape: (214, 20)
Model: "sequential_148"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_297 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_202 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_298 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_203 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_147 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 123ms/step - loss: 9094.2695 - val_loss: 415.5934
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 817.4766 - val_loss: 68.2044
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 223.5820 - val_loss: 26.5361
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 63.4532 - val_loss: 4.5100
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 11.2580 - val_loss: 0.4124
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 3.6658 - val_loss: 0.6942
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 1.6783 - val_loss: 0.1331
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.8753 - val_loss: 0.0372
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.6898 - val_loss: 0.0598
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.5096 - val_loss: 0.0233
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.5203 - val_loss: 0.0736
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.1902 - val_loss: 0.0234
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.2236 - val_loss: 0.3478
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.2463 - val_loss: 0.0554
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.1667 - val_loss: 0.0756
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.2987 - val_loss: 0.1157
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.1687 - val_loss: 0.0059
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 0.1190 - val_loss: 0.2646
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.2618 - val_loss: 0.0237
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.1134 - val_loss: 0.0334
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.1219 - val_loss: 0.0283
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.0577 - val_loss: 0.1656
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.1342 - val_loss: 0.1053
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 0.0950 - val_loss: 0.0247
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.1517 - val_loss: 0.0077
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 0.0615 - val_loss: 0.1031
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.1172 - val_loss: 0.0291
49/49 ━━━━━━━━━━━━━━━━━━━━ 2s 38ms/step 
Sample raw predictions (after inverse transform and clipping): [0.7828205  0.94615453 1.2767978  0.6047141  1.3725545 ]
RMSE =  1.471043
Validation R-squared for item 2416: -1.8352313041687012
53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 35ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=40.2111701965332
True y_val range (after inverse transform): min=0.0, max=10.0
No description has been provided for this image
-----------------------------------
Current item is  1144
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.9
x_eval_time shape before reshape: (1633, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1913, 14)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (208, 20)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (192, 20)
Model: "sequential_149"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_299 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_204 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_300 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_205 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_148 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 15s 128ms/step - loss: 13991.3867 - val_loss: 1560.0195
Epoch 2/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 2702.5930 - val_loss: 203.7880
Epoch 3/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 673.2391 - val_loss: 93.6143
Epoch 4/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 262.7306 - val_loss: 37.9598
Epoch 5/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 121.4239 - val_loss: 18.7477
Epoch 6/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 61.3078 - val_loss: 9.5012
Epoch 7/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 30.0997 - val_loss: 4.7299
Epoch 8/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 17.9337 - val_loss: 1.7755
Epoch 9/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 8.0625 - val_loss: 0.6316
Epoch 10/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 3.9434 - val_loss: 0.3600
Epoch 11/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 2.0804 - val_loss: 0.3424
Epoch 12/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 1.4151 - val_loss: 0.2496
Epoch 13/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.9892 - val_loss: 0.1777
Epoch 14/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.8307 - val_loss: 0.1760
Epoch 15/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.5506 - val_loss: 0.1155
Epoch 16/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.4888 - val_loss: 0.1034
Epoch 17/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 5.2468 - val_loss: 0.2718
Epoch 18/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.8818 - val_loss: 0.0735
Epoch 19/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 0.5547 - val_loss: 0.0859
Epoch 20/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.5271 - val_loss: 0.0386
Epoch 21/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 0.4540 - val_loss: 0.0330
Epoch 22/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 128ms/step - loss: 0.2596 - val_loss: 0.0387
Epoch 23/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.2260 - val_loss: 0.0341
Epoch 24/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.2093 - val_loss: 0.0242
Epoch 25/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.1605 - val_loss: 0.0276
Epoch 26/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1491 - val_loss: 0.0251
Epoch 27/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 128ms/step - loss: 0.1297 - val_loss: 0.0288
Epoch 28/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1378 - val_loss: 0.1528
Epoch 29/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1267 - val_loss: 0.0164
Epoch 30/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 0.0927 - val_loss: 0.0140
Epoch 31/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.0844 - val_loss: 0.0131
Epoch 32/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.0760 - val_loss: 0.0150
Epoch 33/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.0777 - val_loss: 0.0288
Epoch 34/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 0.0765 - val_loss: 0.0817
Epoch 35/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.0841 - val_loss: 0.0966
Epoch 36/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.1430 - val_loss: 0.0739
Epoch 37/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.0872 - val_loss: 0.0452
Epoch 38/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.1513 - val_loss: 0.0465
Epoch 39/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 0.1126 - val_loss: 0.1210
Epoch 40/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.0793 - val_loss: 0.2476
Epoch 41/100
105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 128ms/step - loss: 0.1122 - val_loss: 0.0142
54/54 ━━━━━━━━━━━━━━━━━━━━ 2s 39ms/step  
Sample raw predictions (after inverse transform and clipping): [1.1958846 1.1057892 0.        0.8252209 0.       ]
RMSE =  0.94303274
Validation R-squared for item 1144: -0.20381522178649902
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 34ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=4.8194427490234375
True y_val range (after inverse transform): min=0.0, max=9.0
No description has been provided for this image
-----------------------------------
Current item is  2054
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6000000000000001
x_eval_time shape before reshape: (1627, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1907, 14)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (169, 20)
store_x_eval.shape: (208, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (169, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (194, 20)
Model: "sequential_150"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_301 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_206 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_302 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_207 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_149 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 20s 128ms/step - loss: 27475.6855 - val_loss: 385.4841
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 1731.7477 - val_loss: 145.0212
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 376.2201 - val_loss: 36.6563
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 182.3835 - val_loss: 14.9190
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 79.0177 - val_loss: 5.6637
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 48.6424 - val_loss: 3.9952
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 23.4958 - val_loss: 0.7651
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 11.3417 - val_loss: 1.3950
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 8.4354 - val_loss: 2.6701
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 6.2758 - val_loss: 0.6920
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 4.9987 - val_loss: 0.0691
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 3.7068 - val_loss: 0.2098
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 3.4165 - val_loss: 0.0481
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 2.8183 - val_loss: 0.3777
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 2.7648 - val_loss: 0.1820
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 2.4915 - val_loss: 0.1731
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 595.6979 - val_loss: 74.5545
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 156.7945 - val_loss: 0.0931
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 5.0259 - val_loss: 0.0257
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 1.5664 - val_loss: 0.1607
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 2.7072 - val_loss: 0.0241
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.6687 - val_loss: 0.1009
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.5952 - val_loss: 0.0546
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 0.5457 - val_loss: 0.0326
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.3436 - val_loss: 0.0229
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.3451 - val_loss: 0.0460
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.3660 - val_loss: 0.0091
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.2294 - val_loss: 0.0457
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.3980 - val_loss: 0.0094
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.1786 - val_loss: 0.0465
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.4097 - val_loss: 0.0311
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1797 - val_loss: 0.0082
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.2064 - val_loss: 0.0175
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 0.1093 - val_loss: 0.0116
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1666 - val_loss: 0.0126
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 0.0948 - val_loss: 0.0307
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1528 - val_loss: 0.0110
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.1298 - val_loss: 0.0131
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.0772 - val_loss: 0.0078
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.0870 - val_loss: 0.0115
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1043 - val_loss: 0.0930
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.1805 - val_loss: 0.0221
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.0689 - val_loss: 0.0487
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.8723 - val_loss: 0.1354
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1192 - val_loss: 0.0574
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.1960 - val_loss: 0.3166
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 1.5531 - val_loss: 0.9376
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.4355 - val_loss: 0.0117
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.0319 - val_loss: 0.0086
50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 39ms/step  
Sample raw predictions (after inverse transform and clipping): [0.22629541 0.39531204 0.40688184 0.5427511  0.15686205]
RMSE =  0.78427696
Validation R-squared for item 2054: -0.034137725830078125
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 35ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.8365244269371033
True y_val range (after inverse transform): min=0.0, max=5.0
No description has been provided for this image
-----------------------------------
Current item is  1331
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=15
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7333333333333333
x_eval_time shape before reshape: (1646, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1926, 14)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (163, 20)
store_x_eval.shape: (217, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (207, 20)
Model: "sequential_151"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_303 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_208 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_304 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_209 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_150 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 124ms/step - loss: 33926.3984 - val_loss: 914.1102
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 4459.9355 - val_loss: 256.1666
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 1583.3724 - val_loss: 170.3104
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 384.4293 - val_loss: 9.6343
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 58.0279 - val_loss: 3.2104
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 22.2286 - val_loss: 1.0264
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 11.5960 - val_loss: 2.0818
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 8.0785 - val_loss: 0.5921
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 6.4235 - val_loss: 0.6774
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 5.6328 - val_loss: 0.8505
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 4.5361 - val_loss: 0.5503
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 4.3147 - val_loss: 3.5263
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 4.4916 - val_loss: 0.4098
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 4.3460 - val_loss: 0.2842
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 3.0457 - val_loss: 3.4340
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 3.1861 - val_loss: 0.2413
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 2.4746 - val_loss: 0.6129
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 2.4666 - val_loss: 2.3787
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 2.6748 - val_loss: 0.3426
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 1.8830 - val_loss: 1.4843
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 2.2513 - val_loss: 0.1697
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 1.8892 - val_loss: 0.1997
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 1.7857 - val_loss: 0.4264
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 1.5469 - val_loss: 0.2181
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 1.6987 - val_loss: 0.2123
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 1.4944 - val_loss: 0.2413
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 1.4271 - val_loss: 0.1537
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 1.3516 - val_loss: 0.8287
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 1.3448 - val_loss: 0.2499
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 1.3195 - val_loss: 0.1503
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 1.1111 - val_loss: 1.8026
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 2.3117 - val_loss: 0.0976
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.9608 - val_loss: 1.1050
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 1.3426 - val_loss: 1.3850
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 1.7060 - val_loss: 0.0908
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.8956 - val_loss: 0.4863
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 1.3033 - val_loss: 0.0869
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.7902 - val_loss: 0.1251
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.7943 - val_loss: 0.1434
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 1.0370 - val_loss: 0.6027
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 1.0427 - val_loss: 0.2717
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.6978 - val_loss: 0.1270
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.6883 - val_loss: 0.8568
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.9052 - val_loss: 0.0723
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 0.6780 - val_loss: 1.1747
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.8080 - val_loss: 0.0827
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.5627 - val_loss: 0.4564
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.6854 - val_loss: 0.2399
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.8789 - val_loss: 0.1600
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.6014 - val_loss: 0.4738
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.5916 - val_loss: 0.9112
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.8351 - val_loss: 0.1387
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 0.2173 - val_loss: 0.0188
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.1577 - val_loss: 0.0449
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.0473 - val_loss: 0.0815
Epoch 56/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.1048 - val_loss: 0.0296
Epoch 57/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.1859 - val_loss: 0.0340
Epoch 58/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 1.2796 - val_loss: 0.0149
Epoch 59/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 0.0680 - val_loss: 0.1390
Epoch 60/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.0594 - val_loss: 0.0846
Epoch 61/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.1815 - val_loss: 0.1873
Epoch 62/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 0.1672 - val_loss: 0.1747
Epoch 63/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1157 - val_loss: 0.0232
Epoch 64/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1144 - val_loss: 0.2917
Epoch 65/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1919 - val_loss: 0.0157
Epoch 66/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.2706 - val_loss: 0.0184
Epoch 67/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 0.0770 - val_loss: 0.0240
Epoch 68/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.0683 - val_loss: 0.2970
53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 38ms/step 
Sample raw predictions (after inverse transform and clipping): [2.0578766 1.9396904 1.3884815 1.9469295 1.4092681]
RMSE =  1.8556609
Validation R-squared for item 1331: -0.5182192325592041
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 35ms/step
Predicted y_val range (after inverse transform and clipping): min=0.24021461606025696, max=3.5960328578948975
True y_val range (after inverse transform): min=0.0, max=11.0
No description has been provided for this image
-----------------------------------
Current item is  2377
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=17
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5882352941176471
x_eval_time shape before reshape: (1605, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1885, 14)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (209, 20)
store_x_eval.shape: (173, 20)
store_x_eval.shape: (168, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (200, 20)
Model: "sequential_152"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_305 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_210 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_306 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_211 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_151 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 129ms/step - loss: 4183.4238 - val_loss: 61.1546
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 125.9009 - val_loss: 6.4626
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 24.7645 - val_loss: 1.6798
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 8.9117 - val_loss: 0.5688
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 4.4383 - val_loss: 0.3184
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 2.0638 - val_loss: 0.1638
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 1.2701 - val_loss: 0.1484
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 0.8496 - val_loss: 0.0511
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.4806 - val_loss: 0.0154
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.4489 - val_loss: 0.0086
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.2087 - val_loss: 0.0204
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.2262 - val_loss: 0.0363
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.2234 - val_loss: 0.0065
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.1551 - val_loss: 0.0049
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.1147 - val_loss: 0.0031
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.1036 - val_loss: 0.0042
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.1305 - val_loss: 0.0019
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.1214 - val_loss: 0.0251
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.0785 - val_loss: 0.0095
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.1116 - val_loss: 0.0457
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.0568 - val_loss: 0.0035
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.0443 - val_loss: 0.0346
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1041 - val_loss: 5.7372e-04
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.1081 - val_loss: 0.0351
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.0428 - val_loss: 0.0235
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 1.6046 - val_loss: 0.0064
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 0.2737 - val_loss: 0.0024
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1303 - val_loss: 0.0063
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.0509 - val_loss: 0.0014
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.0419 - val_loss: 5.9376e-04
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.0357 - val_loss: 9.2390e-04
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.0159 - val_loss: 0.0012
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.0273 - val_loss: 7.9883e-04
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 38ms/step 
Sample raw predictions (after inverse transform and clipping): [0.         0.12574503 0.         0.08638018 0.12740503]
RMSE =  0.67207265
Validation R-squared for item 2377: -0.11461794376373291
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 35ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.837878942489624
True y_val range (after inverse transform): min=0.0, max=10.0
No description has been provided for this image
-----------------------------------
Current item is  2124
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=13
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5384615384615385
x_eval_time shape before reshape: (1640, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1920, 14)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (181, 20)
Model: "sequential_153"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_307 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_212 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_308 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_213 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_152 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 131ms/step - loss: 77748.6953 - val_loss: 8623.4561
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 18687.0879 - val_loss: 220.3259
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 1667.3567 - val_loss: 58.0999
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 495.5660 - val_loss: 26.3111
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 276.6446 - val_loss: 27.3185
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 201.9171 - val_loss: 16.6310
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 186.8736 - val_loss: 17.3321
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 128.8895 - val_loss: 19.3532
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 56.3507 - val_loss: 1.9922
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 30.2316 - val_loss: 1.3769
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 27.8897 - val_loss: 1.8490
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 21.0099 - val_loss: 2.3165
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 20.1979 - val_loss: 0.8273
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 17.4124 - val_loss: 0.7523
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 14.0294 - val_loss: 1.8645
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 14.6008 - val_loss: 8.3957
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 12.8100 - val_loss: 6.7560
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 11.9072 - val_loss: 0.6424
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 8.0843 - val_loss: 0.8836
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 7.3480 - val_loss: 0.5911
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 7.3884 - val_loss: 1.5969
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 10.3756 - val_loss: 2.5911
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 8.1578 - val_loss: 0.3387
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 6.8560 - val_loss: 0.5487
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 9.0729 - val_loss: 0.6945
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 4.6878 - val_loss: 0.5543
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 4.5846 - val_loss: 0.5475
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 5.1290 - val_loss: 1.3337
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 3.8830 - val_loss: 2.7092
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 4.9415 - val_loss: 0.3074
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 4.9709 - val_loss: 0.2104
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 3.4889 - val_loss: 5.6649
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 4.2593 - val_loss: 4.3782
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 5.0624 - val_loss: 0.2555
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 5.4932 - val_loss: 0.4498
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 6.7210 - val_loss: 0.9077
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 2.6797 - val_loss: 0.6467
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 4.2507 - val_loss: 0.1367
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 2.0683 - val_loss: 1.6247
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 3.3197 - val_loss: 1.4868
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 2.7358 - val_loss: 0.2415
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 2.3842 - val_loss: 3.6343
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 2.7224 - val_loss: 0.3371
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 1.9495 - val_loss: 0.2440
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 1.8159 - val_loss: 0.4638
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 1.9081 - val_loss: 5.4902
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 2.6322 - val_loss: 0.5447
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 2.1412 - val_loss: 7.7495
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 39ms/step 
Sample raw predictions (after inverse transform and clipping): [0.        3.7810516 2.1341872 0.        0.       ]
RMSE =  2.3113859
Validation R-squared for item 2124: -8.466473579406738
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 36ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=22.486120223999023
True y_val range (after inverse transform): min=0.0, max=7.000000476837158
No description has been provided for this image
-----------------------------------
Current item is  2101
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=6
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5
x_eval_time shape before reshape: (1624, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1904, 14)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (176, 20)
Model: "sequential_154"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_309 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_214 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_310 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_215 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_153 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 131ms/step - loss: 4596.3770 - val_loss: 95.3279
Epoch 2/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 201.1535 - val_loss: 8.1043
Epoch 3/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 48.1081 - val_loss: 2.2798
Epoch 4/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 21.9064 - val_loss: 2.8067
Epoch 5/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 12.0960 - val_loss: 0.7991
Epoch 6/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 6.6001 - val_loss: 0.4042
Epoch 7/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 4.4514 - val_loss: 0.3438
Epoch 8/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 2.7699 - val_loss: 1.0724
Epoch 9/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 2.0883 - val_loss: 0.4742
Epoch 10/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 1.5510 - val_loss: 0.5974
Epoch 11/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 1.0658 - val_loss: 0.0939
Epoch 12/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.8079 - val_loss: 0.0974
Epoch 13/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.5910 - val_loss: 0.0541
Epoch 14/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.5637 - val_loss: 0.2343
Epoch 15/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.4537 - val_loss: 0.0294
Epoch 16/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 0.4189 - val_loss: 1.2565
Epoch 17/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.5113 - val_loss: 0.0727
Epoch 18/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.3016 - val_loss: 0.5314
Epoch 19/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.3707 - val_loss: 0.0054
Epoch 20/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.2245 - val_loss: 0.0476
Epoch 21/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.2129 - val_loss: 0.2784
Epoch 22/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.2542 - val_loss: 0.0435
Epoch 23/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.1911 - val_loss: 0.0103
Epoch 24/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.1833 - val_loss: 0.0141
Epoch 25/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.2151 - val_loss: 0.1160
Epoch 26/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.1724 - val_loss: 0.2750
Epoch 27/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.2971 - val_loss: 0.0051
Epoch 28/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.1426 - val_loss: 0.0401
Epoch 29/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.1889 - val_loss: 0.0094
Epoch 30/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.1121 - val_loss: 0.0686
Epoch 31/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.3033 - val_loss: 0.0050
Epoch 32/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.0776 - val_loss: 0.0053
Epoch 33/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.0852 - val_loss: 0.0754
Epoch 34/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.1178 - val_loss: 0.0974
Epoch 35/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.1675 - val_loss: 0.0064
Epoch 36/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.1183 - val_loss: 0.0184
Epoch 37/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.1062 - val_loss: 0.0494
Epoch 38/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.4449 - val_loss: 0.4500
Epoch 39/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.1324 - val_loss: 0.0079
Epoch 40/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.1745 - val_loss: 0.1996
Epoch 41/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.1124 - val_loss: 0.0072
49/49 ━━━━━━━━━━━━━━━━━━━━ 2s 40ms/step 
Sample raw predictions (after inverse transform and clipping): [0.         0.         0.         0.         0.08559164]
RMSE =  0.4084564
Validation R-squared for item 2101: -1.6216132640838623
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 36ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=3.839362144470215
True y_val range (after inverse transform): min=0.0, max=3.0
No description has been provided for this image
-----------------------------------
Current item is  1343
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=53
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.33962264150943394
x_eval_time shape before reshape: (1581, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1861, 14)
store_x_eval.shape: (169, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (168, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (184, 20)
Model: "sequential_155"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_311 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_216 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_312 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_217 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_154 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 135ms/step - loss: 11506.0781 - val_loss: 34.2771
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 180.9903 - val_loss: 18.6587
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 61.4608 - val_loss: 10.0060
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 29.9688 - val_loss: 2.3365
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 7.8875 - val_loss: 0.4862
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 3.9073 - val_loss: 0.1897
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 1.6753 - val_loss: 0.0762
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 1.1489 - val_loss: 0.3866
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 1.1062 - val_loss: 0.1158
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.6748 - val_loss: 0.0187
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.4190 - val_loss: 0.0112
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.4907 - val_loss: 0.0942
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.2798 - val_loss: 0.0072
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.2238 - val_loss: 0.0058
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.1624 - val_loss: 0.0152
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 0.3529 - val_loss: 0.2461
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 0.1941 - val_loss: 0.0068
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.1258 - val_loss: 0.1571
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.1621 - val_loss: 0.1175
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.1986 - val_loss: 0.0719
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.1441 - val_loss: 0.0986
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.4754 - val_loss: 0.0247
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.3737 - val_loss: 0.0152
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.2390 - val_loss: 0.0378
54/54 ━━━━━━━━━━━━━━━━━━━━ 2s 39ms/step  
Sample raw predictions (after inverse transform and clipping): [0.35500795 2.3920536  2.8993986  3.1933334  2.555277  ]
RMSE =  2.9711812
Validation R-squared for item 1343: -0.7652384042739868
50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 36ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=19.155323028564453
True y_val range (after inverse transform): min=0.0, max=18.0
No description has been provided for this image
-----------------------------------
Current item is  1282
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.1
x_eval_time shape before reshape: (1656, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1936, 14)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (212, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (218, 20)
Model: "sequential_156"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_313 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_218 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_314 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_219 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_155 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 130ms/step - loss: 24126.5449 - val_loss: 37572.9062
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 27283.8320 - val_loss: 210.6883
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 1087.2947 - val_loss: 112.8808
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 318.7560 - val_loss: 79.4087
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 148.5492 - val_loss: 15.4328
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 86.4486 - val_loss: 11.7265
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 55.4236 - val_loss: 14.5651
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 46.3208 - val_loss: 9.2457
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 36.5434 - val_loss: 12.0495
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 22.5853 - val_loss: 27.1561
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 19.8319 - val_loss: 7.3594
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 15.0220 - val_loss: 3.6585
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 12.8204 - val_loss: 2.5105
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 11.2537 - val_loss: 2.6146
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 9.9513 - val_loss: 3.3424
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 11.0742 - val_loss: 19.7970
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 14.1610 - val_loss: 1.0671
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 6.2322 - val_loss: 1.1139
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 5.2772 - val_loss: 0.6742
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 4.7511 - val_loss: 0.5298
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 4.4277 - val_loss: 5.4667
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 4.3241 - val_loss: 1.6676
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 3.4990 - val_loss: 0.5113
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 3.1932 - val_loss: 0.4288
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 2.4783 - val_loss: 1.5986
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 2.7440 - val_loss: 2.5969
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 2.4784 - val_loss: 0.1581
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 1.9538 - val_loss: 0.1912
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 1.6920 - val_loss: 1.8613
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 4.8208 - val_loss: 0.2505
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 1.4823 - val_loss: 0.1437
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 1.6718 - val_loss: 0.3097
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 1.4871 - val_loss: 0.7577
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 1.7606 - val_loss: 1.2359
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 1.4735 - val_loss: 3.7849
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 1.1969 - val_loss: 0.5463
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 1.5938 - val_loss: 4.6894
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 1.7358 - val_loss: 1.6918
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 1.4060 - val_loss: 0.4342
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 1.0667 - val_loss: 5.4104
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 3.5460 - val_loss: 1.2068
54/54 ━━━━━━━━━━━━━━━━━━━━ 2s 40ms/step 
Sample raw predictions (after inverse transform and clipping): [3.8448973 4.3981533 3.9058974 1.7887025 3.7183456]
RMSE =  3.5128472
Validation R-squared for item 1282: -8.491267204284668
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 38ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=23.944820404052734
True y_val range (after inverse transform): min=0.0, max=8.0
No description has been provided for this image
-----------------------------------
Current item is  2071
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=7
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.0
x_eval_time shape before reshape: (1637, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1917, 14)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (176, 20)
store_x_eval.shape: (171, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (236, 20)
store_x_eval.shape: (160, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (221, 20)
Model: "sequential_157"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_315 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_220 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_316 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_221 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_156 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 134ms/step - loss: 17841.7148 - val_loss: 303.4991
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 1005.1638 - val_loss: 1.2742
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 41.4888 - val_loss: 0.4523
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 11.1480 - val_loss: 0.2274
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 4.7374 - val_loss: 0.0675
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 2.9514 - val_loss: 0.0230
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 1.9527 - val_loss: 0.5829
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 1.5042 - val_loss: 0.8634
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 1.7897 - val_loss: 0.6065
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 0.7277 - val_loss: 0.0100
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 0.7133 - val_loss: 0.0103
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.6434 - val_loss: 0.0424
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 0.4902 - val_loss: 0.2382
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.5096 - val_loss: 0.0566
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 0.4524 - val_loss: 0.0172
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 0.2683 - val_loss: 0.1337
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 0.4303 - val_loss: 0.0446
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 1.9005 - val_loss: 0.1519
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.7782 - val_loss: 0.1299
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.4152 - val_loss: 0.9068
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 39ms/step 
Sample raw predictions (after inverse transform and clipping): [0.11523872 0.2424188  0.36801893 0.27300614 0.22022061]
RMSE =  0.7506151
Validation R-squared for item 2071: -0.09216451644897461
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 36ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.4117071628570557
True y_val range (after inverse transform): min=0.0, max=7.0
No description has been provided for this image
-----------------------------------
Current item is  1150
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=5
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8
x_eval_time shape before reshape: (1623, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1903, 14)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (217, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (176, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (187, 20)
Model: "sequential_158"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_317 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_222 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_318 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_223 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_157 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 135ms/step - loss: 11214.9082 - val_loss: 284.7537
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 574.0168 - val_loss: 10.8568
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 135.6079 - val_loss: 8.9586
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 59.2822 - val_loss: 10.3243
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 26.6757 - val_loss: 2.1576
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 16.3684 - val_loss: 1.8340
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 8.9846 - val_loss: 1.2336
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 5.5760 - val_loss: 0.1891
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 3.4110 - val_loss: 0.8386
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 2.1602 - val_loss: 0.3900
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 1.6987 - val_loss: 0.4127
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 1.1926 - val_loss: 0.1861
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 0.7275 - val_loss: 0.1232
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 1.0564 - val_loss: 0.0968
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 0.7844 - val_loss: 0.0675
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 0.6671 - val_loss: 0.1604
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 1.8742 - val_loss: 0.1370
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 1.0914 - val_loss: 0.1966
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 1.2973 - val_loss: 0.3584
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 0.5718 - val_loss: 0.5125
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 0.5923 - val_loss: 0.1127
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.3244 - val_loss: 0.1197
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.2778 - val_loss: 0.0745
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.3903 - val_loss: 0.3648
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.2529 - val_loss: 0.1688
50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 40ms/step 
Sample raw predictions (after inverse transform and clipping): [0. 0. 0. 0. 0.]
RMSE =  0.6855278
Validation R-squared for item 1150: -0.6627413034439087
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 38ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=13.094039916992188
True y_val range (after inverse transform): min=0.0, max=4.0
No description has been provided for this image
-----------------------------------
Current item is  2440
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=52
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8076923076923077
x_eval_time shape before reshape: (1621, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1901, 14)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (169, 20)
store_x_eval.shape: (214, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (206, 20)
Model: "sequential_159"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_319 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_224 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_320 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_225 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_158 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 136ms/step - loss: 9610.1035 - val_loss: 82.6015
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 372.3842 - val_loss: 6.7885
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 73.6376 - val_loss: 3.2610
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 41.8971 - val_loss: 1.2933
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 23.0300 - val_loss: 0.5036
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 17.1519 - val_loss: 0.4794
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 10.2799 - val_loss: 0.1863
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 8.2328 - val_loss: 0.0860
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 6.0093 - val_loss: 0.0664
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 4.3283 - val_loss: 0.2106
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 4.5909 - val_loss: 0.0545
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 2.7136 - val_loss: 0.0518
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 3.1822 - val_loss: 0.1410
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 2.0756 - val_loss: 0.1270
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 1.7022 - val_loss: 0.1522
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 1.4829 - val_loss: 0.0653
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 1.2961 - val_loss: 0.0629
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 1.1700 - val_loss: 0.5049
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 1.0684 - val_loss: 0.1156
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 1.3937 - val_loss: 0.6218
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 5.8864 - val_loss: 0.0995
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 2.2710 - val_loss: 0.1379
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 42ms/step  
Sample raw predictions (after inverse transform and clipping): [16.7485   11.687377  5.408379 10.92798  10.36963 ]
RMSE =  14.551651
Validation R-squared for item 2440: -31.321369171142578
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 39ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=273.25048828125
True y_val range (after inverse transform): min=0.0, max=28.000001907348633
No description has been provided for this image
-----------------------------------
Current item is  2275
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5
x_eval_time shape before reshape: (1628, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1908, 14)
store_x_eval.shape: (210, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (215, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (202, 20)
Model: "sequential_160"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_321 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_226 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_322 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_227 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_159 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 135ms/step - loss: 26791.0273 - val_loss: 29672.2324
Epoch 2/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 53993.0273 - val_loss: 513.0013
Epoch 3/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 673.9023 - val_loss: 31.1196
Epoch 4/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 162.3504 - val_loss: 29.2547
Epoch 5/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 114.7378 - val_loss: 15.9494
Epoch 6/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 100.4408 - val_loss: 22.4459
Epoch 7/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 69.6987 - val_loss: 13.6501
Epoch 8/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 53.8268 - val_loss: 13.0580
Epoch 9/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 47.1084 - val_loss: 11.2153
Epoch 10/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 39.7829 - val_loss: 11.0481
Epoch 11/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 33.9240 - val_loss: 13.1413
Epoch 12/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 31.8186 - val_loss: 8.8661
Epoch 13/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 28.8823 - val_loss: 6.2544
Epoch 14/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 24.9888 - val_loss: 5.6888
Epoch 15/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 22.7323 - val_loss: 7.8480
Epoch 16/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 21.5477 - val_loss: 5.6708
Epoch 17/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 18.8886 - val_loss: 5.5605
Epoch 18/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 17.1236 - val_loss: 7.0068
Epoch 19/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 16.3067 - val_loss: 9.2909
Epoch 20/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 15.0861 - val_loss: 5.3550
Epoch 21/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 13.8907 - val_loss: 3.4807
Epoch 22/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 11.8613 - val_loss: 5.2866
Epoch 23/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 11.5440 - val_loss: 2.2338
Epoch 24/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 10.4030 - val_loss: 2.3944
Epoch 25/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 9.9970 - val_loss: 2.4422
Epoch 26/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 8.9387 - val_loss: 1.7768
Epoch 27/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 8.0716 - val_loss: 1.6753
Epoch 28/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 7.2742 - val_loss: 2.3853
Epoch 29/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 7.0801 - val_loss: 3.3885
Epoch 30/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 6.9125 - val_loss: 6.3852
Epoch 31/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 6.3945 - val_loss: 5.4159
Epoch 32/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 6.9503 - val_loss: 1.1819
Epoch 33/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 5.2416 - val_loss: 5.1948
Epoch 34/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 5.0435 - val_loss: 2.6616
Epoch 35/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 4.9337 - val_loss: 4.0510
Epoch 36/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 4.9732 - val_loss: 0.8749
Epoch 37/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 3.9410 - val_loss: 1.0958
Epoch 38/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 3.5295 - val_loss: 0.4307
Epoch 39/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 3.1372 - val_loss: 1.4634
Epoch 40/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 3.2966 - val_loss: 0.6575
Epoch 41/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 2.8085 - val_loss: 2.1054
Epoch 42/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 3.0266 - val_loss: 0.9561
Epoch 43/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 2.3170 - val_loss: 1.6009
Epoch 44/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 2.7419 - val_loss: 1.3106
Epoch 45/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 1.8112 - val_loss: 0.2412
Epoch 46/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 1.9576 - val_loss: 4.4323
Epoch 47/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 2.1736 - val_loss: 1.0446
Epoch 48/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 2.0368 - val_loss: 0.1654
Epoch 49/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 2.3685 - val_loss: 1.3043
Epoch 50/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 1.6185 - val_loss: 0.6010
Epoch 51/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 1.2893 - val_loss: 0.1511
Epoch 52/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 1.1237 - val_loss: 0.1330
Epoch 53/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 23.8840 - val_loss: 0.2675
Epoch 54/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 1.0607 - val_loss: 0.5778
Epoch 55/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.8438 - val_loss: 0.2836
Epoch 56/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.9333 - val_loss: 0.1179
Epoch 57/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 1.7330 - val_loss: 0.0430
Epoch 58/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.6337 - val_loss: 0.0677
Epoch 59/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 1.4274 - val_loss: 0.0473
Epoch 60/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.7958 - val_loss: 0.5991
Epoch 61/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.5474 - val_loss: 0.1072
Epoch 62/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 2.0788 - val_loss: 0.1270
Epoch 63/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.4372 - val_loss: 0.0207
Epoch 64/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 1.2540 - val_loss: 0.0349
Epoch 65/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 2.0520 - val_loss: 1.5601
Epoch 66/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.3541 - val_loss: 0.0205
Epoch 67/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.5067 - val_loss: 1.8298
Epoch 68/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 0.5464 - val_loss: 0.8860
Epoch 69/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 2.4306 - val_loss: 0.3865
Epoch 70/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.2661 - val_loss: 2.1507
Epoch 71/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.9321 - val_loss: 0.0109
Epoch 72/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.3421 - val_loss: 0.3884
Epoch 73/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 2.1101 - val_loss: 0.1477
Epoch 74/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.6397 - val_loss: 2.3773
Epoch 75/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 1.9887 - val_loss: 0.2332
Epoch 76/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.5272 - val_loss: 0.6523
Epoch 77/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 3.8380 - val_loss: 0.0234
Epoch 78/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 1.9563 - val_loss: 0.0796
Epoch 79/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.0942 - val_loss: 0.0171
Epoch 80/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.0602 - val_loss: 0.0096
Epoch 81/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.0470 - val_loss: 0.0656
Epoch 82/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.3885 - val_loss: 0.0159
Epoch 83/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 1.4250 - val_loss: 0.5390
Epoch 84/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.1683 - val_loss: 0.1114
Epoch 85/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.0751 - val_loss: 0.0492
Epoch 86/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.0600 - val_loss: 0.2197
Epoch 87/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.1675 - val_loss: 0.4599
Epoch 88/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.6170 - val_loss: 0.1672
Epoch 89/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 1.5804 - val_loss: 0.0234
Epoch 90/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.3417 - val_loss: 0.0067
Epoch 91/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.4706 - val_loss: 0.0033
Epoch 92/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.2699 - val_loss: 2.4167
Epoch 93/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 2.0663 - val_loss: 0.0031
Epoch 94/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.1778 - val_loss: 0.0163
Epoch 95/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.7235 - val_loss: 0.3384
Epoch 96/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.1202 - val_loss: 0.0563
Epoch 97/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.2330 - val_loss: 0.0121
Epoch 98/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.1647 - val_loss: 0.0533
Epoch 99/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 42.2987 - val_loss: 0.0082
Epoch 100/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.0202 - val_loss: 0.0060
50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 40ms/step 
Sample raw predictions (after inverse transform and clipping): [0.41470802 0.27433136 0.35833877 0.45431012 0.24084675]
RMSE =  0.67918676
Validation R-squared for item 2275: -0.08710885047912598
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 37ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.7145441174507141
True y_val range (after inverse transform): min=0.0, max=5.0
No description has been provided for this image
-----------------------------------
Current item is  2493
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=5
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.0
x_eval_time shape before reshape: (1697, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1977, 14)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (212, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (213, 20)
Model: "sequential_161"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_323 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_228 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_324 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_229 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_160 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 139ms/step - loss: 10158.6904 - val_loss: 958.8754
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 140ms/step - loss: 689.9169 - val_loss: 7.1501
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 44.1210 - val_loss: 2.0655
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 140ms/step - loss: 16.1113 - val_loss: 0.5033
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 9.5116 - val_loss: 0.2067
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 141ms/step - loss: 6.8276 - val_loss: 0.3904
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 4.6500 - val_loss: 0.1720
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 3.4991 - val_loss: 0.7321
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 2.6640 - val_loss: 0.0727
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 1.7941 - val_loss: 0.3509
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 1.0154 - val_loss: 0.0223
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 140ms/step - loss: 4.3788 - val_loss: 0.0262
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 0.6964 - val_loss: 0.0314
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 0.5533 - val_loss: 0.0584
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 141ms/step - loss: 0.4383 - val_loss: 0.0238
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 0.2738 - val_loss: 0.0783
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 140ms/step - loss: 0.1589 - val_loss: 0.0149
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 0.1620 - val_loss: 0.0114
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 140ms/step - loss: 0.1177 - val_loss: 0.0183
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 0.1248 - val_loss: 0.0338
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 140ms/step - loss: 0.1310 - val_loss: 0.0511
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 0.1507 - val_loss: 0.2860
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 140ms/step - loss: 0.1917 - val_loss: 24.3100
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 2.8185 - val_loss: 0.0176
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 0.1862 - val_loss: 0.0172
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 0.1457 - val_loss: 0.0383
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 0.0805 - val_loss: 0.0228
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.1175 - val_loss: 0.0913
50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step 
Sample raw predictions (after inverse transform and clipping): [0.37514058 0.45209056 0.27968937 0.05441505 0.3064559 ]
RMSE =  0.46462744
Validation R-squared for item 2493: -0.125604510307312
54/54 ━━━━━━━━━━━━━━━━━━━━ 2s 38ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.366655707359314
True y_val range (after inverse transform): min=0.0, max=5.0
No description has been provided for this image
-----------------------------------
Current item is  2295
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8
x_eval_time shape before reshape: (1615, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1895, 14)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (182, 20)
Model: "sequential_162"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_325 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_230 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_326 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_231 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_161 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 6719s 147ms/step - loss: 196283.9375 - val_loss: 23562.9629
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 81273.2812 - val_loss: 1188.6191
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 148ms/step - loss: 7540.7725 - val_loss: 1201.7190
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 145ms/step - loss: 2459.8171 - val_loss: 230.5009
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 148ms/step - loss: 1023.4954 - val_loss: 121.3973
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 741.0065 - val_loss: 82.3422
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 562.4069 - val_loss: 135.8063
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 464.3417 - val_loss: 53.0934
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 146ms/step - loss: 368.8252 - val_loss: 39.1010
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 299.8866 - val_loss: 32.9394
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 148ms/step - loss: 256.9548 - val_loss: 31.7178
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 216.2832 - val_loss: 25.6257
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 194.2388 - val_loss: 22.3754
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 159.1252 - val_loss: 16.0983
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 134.5797 - val_loss: 22.1327
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 149ms/step - loss: 118.8223 - val_loss: 19.1891
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 149ms/step - loss: 99.8904 - val_loss: 10.2108
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 89.4394 - val_loss: 8.3605
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 149ms/step - loss: 78.7821 - val_loss: 7.0579
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 149ms/step - loss: 66.6617 - val_loss: 7.3500
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 61.6112 - val_loss: 6.6963
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 56.3950 - val_loss: 5.2609
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 167ms/step - loss: 48.5325 - val_loss: 4.8061
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 167ms/step - loss: 45.0271 - val_loss: 9.0433
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 44.8252 - val_loss: 7.4727
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 38.7707 - val_loss: 2.6112
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 32.3414 - val_loss: 2.4760
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 28.7651 - val_loss: 2.4817
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 25.6937 - val_loss: 2.0706
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 23.3529 - val_loss: 1.7325
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 21.3614 - val_loss: 1.8803
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 20.4109 - val_loss: 1.5603
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 18.3791 - val_loss: 3.2624
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 17.3158 - val_loss: 4.4511
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 15.6975 - val_loss: 1.0363
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 13.8945 - val_loss: 1.3679
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 166ms/step - loss: 12.0633 - val_loss: 2.0575
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 12.0345 - val_loss: 1.3926
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 11.7324 - val_loss: 0.6213
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 9.3329 - val_loss: 2.9987
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 9.5137 - val_loss: 2.0247
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 8.7396 - val_loss: 3.8678
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 8.0577 - val_loss: 2.2001
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 6.8298 - val_loss: 2.9724
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 166ms/step - loss: 6.3415 - val_loss: 0.3758
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 7.0406 - val_loss: 3.4892
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 5.2286 - val_loss: 0.3595
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 4.8095 - val_loss: 0.8306
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 4.3768 - val_loss: 1.0001
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 3.9822 - val_loss: 0.2089
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 3.6241 - val_loss: 0.6916
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 167ms/step - loss: 3.6527 - val_loss: 0.3809
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 3.9173 - val_loss: 0.5944
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 3.6398 - val_loss: 0.1101
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 2.7412 - val_loss: 0.1193
Epoch 56/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 2.7114 - val_loss: 0.1450
Epoch 57/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 2.4477 - val_loss: 0.4662
Epoch 58/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 1.9086 - val_loss: 1.3868
Epoch 59/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 2.5274 - val_loss: 1.4006
Epoch 60/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 2.2618 - val_loss: 1.7765
Epoch 61/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 2.0438 - val_loss: 0.0796
Epoch 62/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 2.3071 - val_loss: 1.0287
Epoch 63/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 4.1032 - val_loss: 0.0651
Epoch 64/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 1.6788 - val_loss: 1.1049
Epoch 65/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 1.6689 - val_loss: 3.5393
Epoch 66/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 3.8281 - val_loss: 0.4853
Epoch 67/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 1.4185 - val_loss: 4.2163
Epoch 68/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 2.1761 - val_loss: 0.0918
Epoch 69/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 1.4761 - val_loss: 0.0946
Epoch 70/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 2.2497 - val_loss: 0.1770
Epoch 71/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 1.2134 - val_loss: 0.0361
Epoch 72/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.9747 - val_loss: 0.2763
Epoch 73/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 1.5640 - val_loss: 0.8671
Epoch 74/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 62.5000 - val_loss: 0.7759
Epoch 75/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 3.9235 - val_loss: 0.0445
Epoch 76/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 2.4645 - val_loss: 0.0562
Epoch 77/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 0.4999 - val_loss: 0.0472
Epoch 78/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 0.4179 - val_loss: 0.0185
Epoch 79/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 0.3514 - val_loss: 0.0218
Epoch 80/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.3223 - val_loss: 0.0158
Epoch 81/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.2693 - val_loss: 0.0153
Epoch 82/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 141ms/step - loss: 0.3160 - val_loss: 0.1825
Epoch 83/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 0.3695 - val_loss: 0.0332
Epoch 84/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 0.2621 - val_loss: 0.1739
Epoch 85/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 0.2955 - val_loss: 0.0351
Epoch 86/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 0.2795 - val_loss: 0.3956
Epoch 87/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 0.3272 - val_loss: 0.0154
Epoch 88/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 0.2339 - val_loss: 0.0553
Epoch 89/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 0.2795 - val_loss: 0.0333
Epoch 90/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.2222 - val_loss: 0.0271
Epoch 91/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.1631 - val_loss: 0.0374
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step 
Sample raw predictions (after inverse transform and clipping): [0.         0.5100306  0.51845336 0.         0.        ]
RMSE =  0.89877003
Validation R-squared for item 2295: -0.3055284023284912
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 38ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=2.269831895828247
True y_val range (after inverse transform): min=0.0, max=8.0
No description has been provided for this image
-----------------------------------
Current item is  1405
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=5
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.0
x_eval_time shape before reshape: (1592, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1872, 14)
store_x_eval.shape: (170, 20)
store_x_eval.shape: (164, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (171, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (203, 20)
Model: "sequential_163"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_327 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_232 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_328 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_233 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_162 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 139ms/step - loss: 41853.9102 - val_loss: 1703.3793
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 140ms/step - loss: 3186.4482 - val_loss: 172.7683
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 781.2379 - val_loss: 44.5581
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 391.5133 - val_loss: 51.9665
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 236.6488 - val_loss: 24.3202
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 163.3580 - val_loss: 16.4731
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 116.3193 - val_loss: 14.6715
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 141ms/step - loss: 94.3411 - val_loss: 13.1433
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 73.4092 - val_loss: 8.0270
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 60.3665 - val_loss: 6.3872
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 50.8782 - val_loss: 5.1146
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 39.9864 - val_loss: 14.0098
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 32.7865 - val_loss: 7.7327
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 25.9171 - val_loss: 6.9707
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 21.7164 - val_loss: 5.4468
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 18.7399 - val_loss: 6.6654
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 30.8758 - val_loss: 5.9538
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 18.2208 - val_loss: 7.4575
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 13.1441 - val_loss: 2.9795
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 9.1352 - val_loss: 2.8224
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 8.2573 - val_loss: 2.2899
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 6.3347 - val_loss: 3.2073
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 5.4501 - val_loss: 3.6204
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 4.3931 - val_loss: 2.0120
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 3.5196 - val_loss: 1.4187
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 2.8249 - val_loss: 1.5717
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 2.5675 - val_loss: 1.1250
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 2.0641 - val_loss: 0.9739
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 1.8870 - val_loss: 1.2474
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 1.6055 - val_loss: 0.5503
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 1.4476 - val_loss: 1.0856
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 1.2805 - val_loss: 0.4170
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 1.2055 - val_loss: 0.2133
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 1.3914 - val_loss: 0.3030
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.9816 - val_loss: 0.4450
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 1.0530 - val_loss: 0.1596
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.9507 - val_loss: 0.0810
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 0.7638 - val_loss: 0.5577
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.8065 - val_loss: 0.9310
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 0.9308 - val_loss: 0.1987
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.7339 - val_loss: 0.0630
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.6380 - val_loss: 0.2406
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 0.9683 - val_loss: 0.0444
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.5008 - val_loss: 2.4952
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.8973 - val_loss: 0.0383
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.7507 - val_loss: 0.2402
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.8202 - val_loss: 0.2630
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.5695 - val_loss: 0.5742
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.7165 - val_loss: 1.9525
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.9715 - val_loss: 0.1626
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 1.3140 - val_loss: 0.0399
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.3608 - val_loss: 0.1172
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.5038 - val_loss: 2.3071
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 1.1377 - val_loss: 1.1517
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 1.2539 - val_loss: 0.4017
53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step  
Sample raw predictions (after inverse transform and clipping): [0.        0.        0.8184359 0.6945181 0.       ]
RMSE =  0.673594
Validation R-squared for item 1405: -1.0072171688079834
50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 38ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=4.984849452972412
True y_val range (after inverse transform): min=0.0, max=5.0
No description has been provided for this image
-----------------------------------
Current item is  1013
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=20
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8
x_eval_time shape before reshape: (1655, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1935, 14)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (176, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (172, 20)
store_x_eval.shape: (237, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (198, 20)
Model: "sequential_164"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_329 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_234 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_330 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_235 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_163 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 136ms/step - loss: 48021.1875 - val_loss: 10569.5479
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 29628.4316 - val_loss: 422.1935
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 2437.9456 - val_loss: 90.5340
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 847.9769 - val_loss: 41.7940
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 429.5881 - val_loss: 18.7420
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 248.9054 - val_loss: 9.5722
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 184.9539 - val_loss: 6.0886
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 126.2957 - val_loss: 6.0139
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 93.3610 - val_loss: 6.9112
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 76.2517 - val_loss: 5.8735
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 60.3761 - val_loss: 4.1553
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 47.1582 - val_loss: 6.6050
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 38.9448 - val_loss: 4.3873
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 34.2571 - val_loss: 6.4738
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 29.7838 - val_loss: 1.5087
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 31.5257 - val_loss: 1.9249
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 22.1373 - val_loss: 2.9761
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 18.6961 - val_loss: 5.0042
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 18.5326 - val_loss: 1.8502
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 16.8063 - val_loss: 1.2883
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 16.8369 - val_loss: 1.4463
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 12.8103 - val_loss: 0.6492
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 11.4151 - val_loss: 0.8858
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 17.3360 - val_loss: 0.9642
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 7.0109 - val_loss: 0.8893
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 5.8840 - val_loss: 0.6980
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 6.3753 - val_loss: 0.3428
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 6.0170 - val_loss: 0.3516
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 6.0138 - val_loss: 0.4584
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 4.6010 - val_loss: 0.5501
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 4.9507 - val_loss: 0.2627
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 3.7938 - val_loss: 0.2145
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 3.7283 - val_loss: 0.4183
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 4.3152 - val_loss: 0.4099
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 3.1584 - val_loss: 0.1940
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 2.8501 - val_loss: 1.1303
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 3.6839 - val_loss: 0.1793
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 2.5758 - val_loss: 0.0955
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 2.8663 - val_loss: 0.0861
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 3.4693 - val_loss: 0.2041
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 1.5317 - val_loss: 0.0749
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 2.7122 - val_loss: 7.8611
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 33.3038 - val_loss: 4.6301
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 6.5292 - val_loss: 0.2875
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 4.3601 - val_loss: 0.2849
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 2.1717 - val_loss: 0.1102
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 1.5541 - val_loss: 0.2558
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 1.7726 - val_loss: 0.8000
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 2.0804 - val_loss: 0.0835
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 1.3844 - val_loss: 3.8364
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 2.3163 - val_loss: 1.0391
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step 
Sample raw predictions (after inverse transform and clipping): [2.2213855  6.4484763  0.3758602  0.         0.36535102]
RMSE =  4.832603
Validation R-squared for item 1013: -11.724756240844727
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 38ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=54.19609069824219
True y_val range (after inverse transform): min=0.0, max=13.0
No description has been provided for this image
-----------------------------------
Current item is  1423
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=11
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.4545454545454546
x_eval_time shape before reshape: (1556, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1836, 14)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (170, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (173, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (173, 20)
Model: "sequential_165"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_331 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_236 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_332 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_237 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_164 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 144ms/step - loss: 26175.9883 - val_loss: 13301.8105
Epoch 2/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 140ms/step - loss: 10936.4199 - val_loss: 339.5075
Epoch 3/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 481.1048 - val_loss: 37.9467
Epoch 4/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 141ms/step - loss: 120.1431 - val_loss: 15.6336
Epoch 5/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 143.3273 - val_loss: 25.1780
Epoch 6/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 110.5352 - val_loss: 34.6851
Epoch 7/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 45.1754 - val_loss: 7.8212
Epoch 8/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 26.3397 - val_loss: 2.6428
Epoch 9/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 15.0817 - val_loss: 2.0156
Epoch 10/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 11.1229 - val_loss: 1.0675
Epoch 11/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 10.7054 - val_loss: 8.4704
Epoch 12/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 12.4444 - val_loss: 6.7464
Epoch 13/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 8.6343 - val_loss: 1.9086
Epoch 14/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 8.3899 - val_loss: 10.0375
Epoch 15/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 7.7643 - val_loss: 4.0236
Epoch 16/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 7.9887 - val_loss: 1.0783
Epoch 17/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 145ms/step - loss: 7.7469 - val_loss: 0.8902
Epoch 18/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 5.9835 - val_loss: 1.4161
Epoch 19/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 4.8943 - val_loss: 11.5499
Epoch 20/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 5.0112 - val_loss: 0.5473
Epoch 21/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 4.2177 - val_loss: 0.2626
Epoch 22/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 5.5435 - val_loss: 1.7304
Epoch 23/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 3.9700 - val_loss: 0.2638
Epoch 24/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 4.4083 - val_loss: 2.5410
Epoch 25/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 3.8710 - val_loss: 15.3921
Epoch 26/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 6.8947 - val_loss: 4.2897
Epoch 27/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 3.2120 - val_loss: 0.0985
Epoch 28/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 4.9776 - val_loss: 6.6287
Epoch 29/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 4.6643 - val_loss: 1.5703
Epoch 30/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 3.7327 - val_loss: 5.6929
Epoch 31/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 3.6115 - val_loss: 6.5618
Epoch 32/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 5.2497 - val_loss: 0.4556
Epoch 33/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 2.7280 - val_loss: 20.4897
Epoch 34/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 6.6911 - val_loss: 0.4609
Epoch 35/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 2.4141 - val_loss: 1.4230
Epoch 36/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 1.7808 - val_loss: 0.5163
Epoch 37/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 1.7477 - val_loss: 2.0559
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 43ms/step  
Sample raw predictions (after inverse transform and clipping): [0.         2.2831542  0.         0.         0.14877482]
RMSE =  2.3659296
Validation R-squared for item 1423: -28.13811492919922
49/49 ━━━━━━━━━━━━━━━━━━━━ 2s 40ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=13.75198745727539
True y_val range (after inverse transform): min=0.0, max=5.0
No description has been provided for this image
-----------------------------------
Current item is  2067
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=32
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.03125
x_eval_time shape before reshape: (1638, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1918, 14)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (210, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (175, 20)
Model: "sequential_166"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_333 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_238 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_334 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_239 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_165 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 145ms/step - loss: 49758.1289 - val_loss: 4100.7563
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 31404.7949 - val_loss: 359.1188
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 2782.4688 - val_loss: 49.1615
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 932.6702 - val_loss: 23.3819
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 410.5623 - val_loss: 19.1208
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 141ms/step - loss: 217.5927 - val_loss: 15.0354
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 125.4185 - val_loss: 6.6713
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 90.3995 - val_loss: 5.8950
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 81.1977 - val_loss: 11.3496
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 75.9165 - val_loss: 12.2959
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 54.7328 - val_loss: 3.8332
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 38.8506 - val_loss: 2.3428
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 27.3894 - val_loss: 2.4105
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 21.1730 - val_loss: 1.7291
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 17.8600 - val_loss: 1.4781
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 13.2840 - val_loss: 0.8020
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 12.2094 - val_loss: 0.9192
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 145ms/step - loss: 9.9272 - val_loss: 3.1766
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 8.2222 - val_loss: 1.4799
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 7.1446 - val_loss: 2.3803
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 7.9671 - val_loss: 1.0391
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 39.6769 - val_loss: 0.4123
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 4.8794 - val_loss: 0.8115
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 4.8155 - val_loss: 0.3322
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 3.9087 - val_loss: 0.3938
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 3.3881 - val_loss: 0.3961
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 3.0871 - val_loss: 0.2012
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 146ms/step - loss: 2.8197 - val_loss: 0.1701
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 2.4934 - val_loss: 0.1227
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 2.0381 - val_loss: 0.5651
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 2.0195 - val_loss: 0.2722
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 149ms/step - loss: 1.5995 - val_loss: 0.5085
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 146ms/step - loss: 1.5628 - val_loss: 0.0709
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 1.3375 - val_loss: 0.2729
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 1.2282 - val_loss: 0.4542
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 1.1069 - val_loss: 0.0855
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 1.1198 - val_loss: 0.1040
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 1.0516 - val_loss: 0.0512
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.8213 - val_loss: 0.1911
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.8091 - val_loss: 0.0524
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.7329 - val_loss: 0.0475
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.5727 - val_loss: 0.0387
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.6283 - val_loss: 0.1047
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.5129 - val_loss: 0.2841
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.4641 - val_loss: 0.0223
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.5807 - val_loss: 0.1022
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.3882 - val_loss: 0.0187
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.3226 - val_loss: 0.0307
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 155ms/step - loss: 0.3402 - val_loss: 0.0190
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.3338 - val_loss: 0.0337
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.2210 - val_loss: 0.0448
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 149ms/step - loss: 0.2258 - val_loss: 0.0107
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 0.1926 - val_loss: 0.1086
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 0.2295 - val_loss: 0.0117
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 146ms/step - loss: 0.2332 - val_loss: 0.0488
Epoch 56/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 145ms/step - loss: 0.3337 - val_loss: 0.0081
Epoch 57/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 0.1533 - val_loss: 0.0081
Epoch 58/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 0.1585 - val_loss: 0.0369
Epoch 59/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 145ms/step - loss: 0.1328 - val_loss: 0.0321
Epoch 60/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 146ms/step - loss: 0.1303 - val_loss: 0.0299
Epoch 61/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 145ms/step - loss: 2.1771 - val_loss: 0.0161
Epoch 62/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 0.0920 - val_loss: 0.0225
Epoch 63/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.2446 - val_loss: 0.0201
Epoch 64/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 166ms/step - loss: 0.0889 - val_loss: 0.0060
Epoch 65/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.1404 - val_loss: 0.0067
Epoch 66/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.1306 - val_loss: 0.0015
Epoch 67/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.1145 - val_loss: 0.0056
Epoch 68/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0889 - val_loss: 0.0434
Epoch 69/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.1677 - val_loss: 0.0363
Epoch 70/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 156ms/step - loss: 0.0551 - val_loss: 7.8802e-04
Epoch 71/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.1014 - val_loss: 0.0025
Epoch 72/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0297 - val_loss: 0.0011
Epoch 73/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0484 - val_loss: 0.0036
Epoch 74/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.1489 - val_loss: 0.0020
Epoch 75/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0928 - val_loss: 0.0144
Epoch 76/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0631 - val_loss: 0.0047
Epoch 77/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.1275 - val_loss: 0.0043
Epoch 78/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.0142 - val_loss: 0.0024
Epoch 79/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.0353 - val_loss: 0.0025
Epoch 80/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0314 - val_loss: 0.0259
52/52 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step  
Sample raw predictions (after inverse transform and clipping): [0.         0.         0.67165166 0.15849692 0.32690185]
RMSE =  1.3307827
Validation R-squared for item 2067: -0.3342738151550293
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 44ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=2.9452760219573975
True y_val range (after inverse transform): min=0.0, max=33.0
No description has been provided for this image
-----------------------------------
Current item is  2286
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5
x_eval_time shape before reshape: (1579, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1859, 14)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (165, 20)
Model: "sequential_167"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_335 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_240 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_336 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_241 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_166 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 19s 165ms/step - loss: 8558.5498 - val_loss: 63.7728
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 134.5322 - val_loss: 20.0135
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 30.3057 - val_loss: 9.8294
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 17.3734 - val_loss: 4.8284
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 11.0160 - val_loss: 1.3612
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 6.2297 - val_loss: 0.9176
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 4.5474 - val_loss: 0.5704
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 3.5358 - val_loss: 0.5308
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 2.4426 - val_loss: 0.2971
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 1.4475 - val_loss: 0.2013
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 1.0841 - val_loss: 0.1666
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.6682 - val_loss: 0.0908
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.5576 - val_loss: 0.0618
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.6013 - val_loss: 0.1127
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.2955 - val_loss: 0.0344
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.3005 - val_loss: 0.1153
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.2874 - val_loss: 0.0360
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.6016 - val_loss: 0.0137
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.2850 - val_loss: 0.0165
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.3283 - val_loss: 0.0262
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 165ms/step - loss: 0.2247 - val_loss: 0.0065
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 166ms/step - loss: 0.1603 - val_loss: 0.0246
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.1884 - val_loss: 0.1445
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.2328 - val_loss: 0.0050
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 0.1305 - val_loss: 0.1265
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.2113 - val_loss: 0.0203
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 172ms/step - loss: 0.1554 - val_loss: 0.0042
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.1030 - val_loss: 0.0352
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.1020 - val_loss: 0.0076
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.0879 - val_loss: 0.0035
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0999 - val_loss: 0.1310
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.1749 - val_loss: 0.0553
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.1530 - val_loss: 0.0302
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.2074 - val_loss: 0.0106
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.1990 - val_loss: 0.0030
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.1055 - val_loss: 0.0023
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0636 - val_loss: 0.0067
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.0645 - val_loss: 0.0366
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.0822 - val_loss: 0.0221
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0877 - val_loss: 0.0037
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0310 - val_loss: 0.0302
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0365 - val_loss: 0.0118
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0603 - val_loss: 0.0089
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.0975 - val_loss: 0.1860
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 0.4306 - val_loss: 0.0026
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 169ms/step - loss: 0.1040 - val_loss: 0.7465
52/52 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step  
Sample raw predictions (after inverse transform and clipping): [0. 0. 0. 0. 0.]
RMSE =  0.52935505
Validation R-squared for item 2286: -0.4546995162963867
50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 44ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=8.565644264221191
True y_val range (after inverse transform): min=0.0, max=5.0
No description has been provided for this image
-----------------------------------
Current item is  2129
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=6
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.0
x_eval_time shape before reshape: (1636, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1916, 14)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (170, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (214, 20)
store_x_eval.shape: (164, 20)
store_x_eval.shape: (203, 20)
Model: "sequential_168"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_337 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_242 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_338 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_243 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_167 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 150ms/step - loss: 3730.8979 - val_loss: 43.3244
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 102.9349 - val_loss: 6.8940
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 29.9634 - val_loss: 0.7394
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 6.6715 - val_loss: 0.9451
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 6.0368 - val_loss: 0.2754
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 2.2330 - val_loss: 0.0953
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 1.1339 - val_loss: 0.0423
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.8631 - val_loss: 0.0169
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.5747 - val_loss: 0.0250
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.5116 - val_loss: 0.0209
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.3482 - val_loss: 0.0371
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.3904 - val_loss: 0.0459
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.3264 - val_loss: 0.0147
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 170ms/step - loss: 0.2669 - val_loss: 0.1919
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 172ms/step - loss: 0.1827 - val_loss: 0.0559
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.1396 - val_loss: 0.0311
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 167ms/step - loss: 0.1046 - val_loss: 0.0196
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0844 - val_loss: 0.0059
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0741 - val_loss: 0.0198
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0730 - val_loss: 0.1273
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.1103 - val_loss: 0.0154
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0567 - val_loss: 0.1748
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0908 - val_loss: 0.0058
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0768 - val_loss: 0.1383
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0445 - val_loss: 0.0177
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0339 - val_loss: 0.0132
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0364 - val_loss: 0.0058
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.0596 - val_loss: 0.0084
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0431 - val_loss: 0.0077
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0363 - val_loss: 0.0210
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0278 - val_loss: 0.0404
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0427 - val_loss: 0.0240
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0678 - val_loss: 0.0431
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0278 - val_loss: 0.0317
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0247 - val_loss: 0.0223
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0275 - val_loss: 0.0054
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0898 - val_loss: 0.0071
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0184 - val_loss: 0.0219
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0326 - val_loss: 0.0055
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0129 - val_loss: 0.0093
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.0238 - val_loss: 0.0114
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0238 - val_loss: 0.0267
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0144 - val_loss: 0.0083
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0199 - val_loss: 0.0253
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0215 - val_loss: 0.0096
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0135 - val_loss: 0.0117
52/52 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step  
Sample raw predictions (after inverse transform and clipping): [0.12682389 0.14391771 0.13432156 0.08109893 0.03266741]
RMSE =  0.49136835
Validation R-squared for item 2129: 0.022559702396392822
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 43ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.37823107838630676
True y_val range (after inverse transform): min=0.0, max=6.0
No description has been provided for this image
-----------------------------------
Current item is  1082
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=15
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.9333333333333333
x_eval_time shape before reshape: (1637, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1917, 14)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (165, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (194, 20)
Model: "sequential_169"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_339 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_244 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_340 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_245 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_168 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 156ms/step - loss: 22520.6328 - val_loss: 1209.0499
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 1057.8999 - val_loss: 120.9671
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 250.2575 - val_loss: 23.9698
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 66.1376 - val_loss: 2.2222
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 11.5387 - val_loss: 1.1044
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 5.6733 - val_loss: 0.5026
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 3.5553 - val_loss: 0.3384
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 2.7508 - val_loss: 0.2288
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 2.2894 - val_loss: 0.0988
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.9104 - val_loss: 0.3713
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.9204 - val_loss: 0.0776
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.7144 - val_loss: 0.0533
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.6194 - val_loss: 0.0510
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.5294 - val_loss: 0.1150
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.5974 - val_loss: 0.1812
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.3780 - val_loss: 0.1902
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.3830 - val_loss: 0.0934
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.2820 - val_loss: 0.0476
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.2773 - val_loss: 0.0890
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.2824 - val_loss: 0.1307
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.3207 - val_loss: 0.0485
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.2583 - val_loss: 0.2168
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.2167 - val_loss: 0.1284
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.1843 - val_loss: 0.3445
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.4737 - val_loss: 0.0180
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.2394 - val_loss: 0.0236
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.1223 - val_loss: 0.0569
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.1797 - val_loss: 0.0181
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.1550 - val_loss: 0.0278
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.1193 - val_loss: 0.0374
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.2710 - val_loss: 0.0136
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0913 - val_loss: 0.0126
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.0488 - val_loss: 0.0371
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.0573 - val_loss: 0.0262
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 156ms/step - loss: 0.1713 - val_loss: 0.0228
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.0810 - val_loss: 0.1374
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.1062 - val_loss: 0.1152
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.0835 - val_loss: 0.0737
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.0861 - val_loss: 0.1326
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.1285 - val_loss: 0.1604
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.1029 - val_loss: 0.0760
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.1219 - val_loss: 0.0192
51/51 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step  
Sample raw predictions (after inverse transform and clipping): [1.612508   1.4084543  0.5608609  1.3006264  0.14584604]
RMSE =  1.698187
Validation R-squared for item 1082: -0.18663406372070312
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 43ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=6.648131847381592
True y_val range (after inverse transform): min=0.0, max=14.0
No description has been provided for this image
-----------------------------------
Current item is  1254
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=57
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.631578947368421
x_eval_time shape before reshape: (1610, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1890, 14)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (169, 20)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (168, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (199, 20)
Model: "sequential_170"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_341 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_246 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_342 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_247 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_169 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 19s 161ms/step - loss: 15933.2725 - val_loss: 941.1097
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 4106.5435 - val_loss: 67.1737
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 496.1465 - val_loss: 18.8257
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 175.1661 - val_loss: 13.0893
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 83.0492 - val_loss: 7.7522
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 166ms/step - loss: 47.6428 - val_loss: 4.6468
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 23.6646 - val_loss: 2.5897
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 15.2227 - val_loss: 1.2845
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 14.9872 - val_loss: 4.7233
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 13.0479 - val_loss: 4.2775
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 13.6110 - val_loss: 1.1205
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 6.4176 - val_loss: 0.9462
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 166ms/step - loss: 4.0936 - val_loss: 0.4405
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 2.9331 - val_loss: 0.5176
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 2.2207 - val_loss: 0.3241
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 1.8981 - val_loss: 0.8755
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 1.5066 - val_loss: 0.2130
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 1.6624 - val_loss: 0.4517
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 1.0594 - val_loss: 0.2588
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 166ms/step - loss: 1.0668 - val_loss: 0.6110
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 169ms/step - loss: 0.9924 - val_loss: 0.1931
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 169ms/step - loss: 0.9408 - val_loss: 2.1674
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.9311 - val_loss: 0.6126
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 0.7298 - val_loss: 0.1147
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 0.6686 - val_loss: 0.0926
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.5966 - val_loss: 0.0931
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.7041 - val_loss: 1.0019
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 0.9191 - val_loss: 0.0438
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.4458 - val_loss: 0.3220
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 0.5400 - val_loss: 0.2016
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.3742 - val_loss: 0.0333
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.3590 - val_loss: 0.4395
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.3602 - val_loss: 0.1078
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.3841 - val_loss: 0.0225
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.2899 - val_loss: 0.0278
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.2120 - val_loss: 1.2044
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.4807 - val_loss: 0.0838
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.1812 - val_loss: 0.4819
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.5412 - val_loss: 0.0161
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.1966 - val_loss: 0.4338
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.5745 - val_loss: 0.1091
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.1817 - val_loss: 0.0155
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.1565 - val_loss: 0.0110
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.1621 - val_loss: 1.0604
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.3572 - val_loss: 0.7474
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.2541 - val_loss: 0.1619
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.1451 - val_loss: 0.0151
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.4166 - val_loss: 0.0084
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.2506 - val_loss: 0.0565
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.1418 - val_loss: 0.0323
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 0.1150 - val_loss: 1.2632
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.3109 - val_loss: 0.0096
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.1934 - val_loss: 0.1209
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.1930 - val_loss: 0.1396
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0969 - val_loss: 0.0852
Epoch 56/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0829 - val_loss: 0.0227
Epoch 57/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.1642 - val_loss: 0.0157
Epoch 58/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.1037 - val_loss: 0.0111
52/52 ━━━━━━━━━━━━━━━━━━━━ 3s 49ms/step  
Sample raw predictions (after inverse transform and clipping): [ 4.533003   3.0275521 10.059122   2.091683   7.482409 ]
RMSE =  5.100913
Validation R-squared for item 1254: -0.1990525722503662
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 43ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=18.20879364013672
True y_val range (after inverse transform): min=0.0, max=36.0
No description has been provided for this image
-----------------------------------
Current item is  2257
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6000000000000001
x_eval_time shape before reshape: (1642, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1922, 14)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (215, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (171, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (209, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (179, 20)
Model: "sequential_171"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_343 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_248 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_344 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_249 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_170 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 20s 168ms/step - loss: 7655.8535 - val_loss: 780.7281
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 698.8498 - val_loss: 8.7090
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 41.9981 - val_loss: 2.2838
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 19.0797 - val_loss: 1.8406
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 12.3102 - val_loss: 0.7391
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 6.7863 - val_loss: 0.5995
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 5.6349 - val_loss: 0.3972
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 3.8613 - val_loss: 0.5598
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 2.5730 - val_loss: 0.2595
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 2.3194 - val_loss: 0.1624
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 1.8419 - val_loss: 0.2326
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 1.3443 - val_loss: 0.1067
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 1.2257 - val_loss: 0.0792
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.7111 - val_loss: 0.3300
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.8048 - val_loss: 0.1224
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.4845 - val_loss: 0.0704
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.7364 - val_loss: 0.1918
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 2.0593 - val_loss: 0.1270
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.7509 - val_loss: 0.0614
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.5360 - val_loss: 0.4027
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.4971 - val_loss: 0.2346
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.4761 - val_loss: 0.2699
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.4675 - val_loss: 0.0732
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.3231 - val_loss: 0.0496
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.2880 - val_loss: 0.1316
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.4163 - val_loss: 0.0475
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.3311 - val_loss: 0.0918
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.2660 - val_loss: 0.0283
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.2550 - val_loss: 0.1645
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.5115 - val_loss: 0.0430
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.2554 - val_loss: 0.0264
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.1502 - val_loss: 0.0107
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.2469 - val_loss: 0.0218
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.1570 - val_loss: 0.0240
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.1391 - val_loss: 0.0875
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.1865 - val_loss: 0.0331
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.1772 - val_loss: 0.0109
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.1245 - val_loss: 0.0426
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.2395 - val_loss: 0.4324
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.2072 - val_loss: 0.0731
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.1419 - val_loss: 0.0205
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.2081 - val_loss: 0.0794
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step 
Sample raw predictions (after inverse transform and clipping): [1.0130391  0.5801868  0.42985553 0.3100687  0.19320995]
RMSE =  1.0275563
Validation R-squared for item 2257: -0.4919395446777344
52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 43ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=15.472684860229492
True y_val range (after inverse transform): min=0.0, max=6.0
No description has been provided for this image
-----------------------------------
Current item is  2226
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=7
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7142857142857142
x_eval_time shape before reshape: (1670, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1950, 14)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (213, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (164, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (202, 20)
Model: "sequential_172"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_345 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_250 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_346 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_251 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_171 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 18s 152ms/step - loss: 19788.1855 - val_loss: 274.8459
Epoch 2/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 1005.2773 - val_loss: 42.6862
Epoch 3/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 296.0948 - val_loss: 24.6790
Epoch 4/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 166.2050 - val_loss: 12.2961
Epoch 5/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 109.4815 - val_loss: 5.9555
Epoch 6/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 68.3742 - val_loss: 4.3653
Epoch 7/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 43.0775 - val_loss: 1.7519
Epoch 8/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 31.2232 - val_loss: 3.2773
Epoch 9/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 23.2122 - val_loss: 2.0105
Epoch 10/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 23.7326 - val_loss: 1.4713
Epoch 11/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 21.1586 - val_loss: 0.9883
Epoch 12/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 14.4843 - val_loss: 0.6904
Epoch 13/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 10.4123 - val_loss: 0.2270
Epoch 14/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 4.9396 - val_loss: 0.4499
Epoch 15/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 3.4180 - val_loss: 0.1340
Epoch 16/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 2.9707 - val_loss: 0.1373
Epoch 17/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 1.8369 - val_loss: 0.2318
Epoch 18/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 154ms/step - loss: 1.6513 - val_loss: 0.1853
Epoch 19/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 1.2508 - val_loss: 0.2720
Epoch 20/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 1.0896 - val_loss: 0.0603
Epoch 21/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.9523 - val_loss: 0.0671
Epoch 22/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 0.9589 - val_loss: 0.0534
Epoch 23/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 2.7293 - val_loss: 0.6142
Epoch 24/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 4.1293 - val_loss: 0.4313
Epoch 25/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 2.1810 - val_loss: 0.3205
Epoch 26/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.9458 - val_loss: 0.6609
Epoch 27/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 0.6357 - val_loss: 0.0428
Epoch 28/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 0.6377 - val_loss: 0.0426
Epoch 29/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.3435 - val_loss: 5.8038
Epoch 30/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 2.0712 - val_loss: 0.0968
Epoch 31/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.2795 - val_loss: 0.0191
Epoch 32/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 0.2409 - val_loss: 0.0169
Epoch 33/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.1859 - val_loss: 0.2902
Epoch 34/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.3451 - val_loss: 0.0753
Epoch 35/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.1169 - val_loss: 0.0174
Epoch 36/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.1819 - val_loss: 0.1132
Epoch 37/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.2691 - val_loss: 0.0608
Epoch 38/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 1.1690 - val_loss: 0.1198
Epoch 39/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.3410 - val_loss: 0.0618
Epoch 40/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 1.4134 - val_loss: 0.0743
Epoch 41/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 9.3544 - val_loss: 719.4838
Epoch 42/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 149ms/step - loss: 139.1706 - val_loss: 0.3270
47/47 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step 
Sample raw predictions (after inverse transform and clipping): [0.41771007 1.195912   0.9953283  0.91815364 1.08146   ]
RMSE =  0.8685413
Validation R-squared for item 2226: -0.8839174509048462
53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 42ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=3.5996005535125732
True y_val range (after inverse transform): min=0.0, max=5.0
No description has been provided for this image
-----------------------------------
Current item is  1283
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=28
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.46428571428571425
x_eval_time shape before reshape: (1604, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1884, 14)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (213, 20)
store_x_eval.shape: (176, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (183, 20)
Model: "sequential_173"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_347 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_252 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_348 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_253 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_172 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 146ms/step - loss: 2103.4707 - val_loss: 13.8779
Epoch 2/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 77.4016 - val_loss: 3.1525
Epoch 3/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 149ms/step - loss: 8.4946 - val_loss: 0.2621
Epoch 4/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 1.2050 - val_loss: 0.2581
Epoch 5/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 145ms/step - loss: 0.5822 - val_loss: 0.0142
Epoch 6/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 0.2352 - val_loss: 0.2116
Epoch 7/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 0.2359 - val_loss: 0.0146
Epoch 8/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 149ms/step - loss: 0.1990 - val_loss: 0.0643
Epoch 9/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 155ms/step - loss: 0.1490 - val_loss: 0.0518
Epoch 10/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 154ms/step - loss: 0.1222 - val_loss: 0.0526
Epoch 11/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.1653 - val_loss: 0.0120
Epoch 12/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.0974 - val_loss: 0.2396
Epoch 13/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0988 - val_loss: 0.0058
Epoch 14/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.1241 - val_loss: 0.1660
Epoch 15/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.1062 - val_loss: 0.2396
Epoch 16/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 155ms/step - loss: 0.1304 - val_loss: 0.0111
Epoch 17/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0658 - val_loss: 0.1949
Epoch 18/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 155ms/step - loss: 0.0856 - val_loss: 0.1554
Epoch 19/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.1312 - val_loss: 0.0049
Epoch 20/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.0549 - val_loss: 0.1113
Epoch 21/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0634 - val_loss: 0.0460
Epoch 22/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 146ms/step - loss: 0.0605 - val_loss: 0.1674
Epoch 23/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0734 - val_loss: 0.1554
Epoch 24/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.0830 - val_loss: 0.6487
Epoch 25/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.1663 - val_loss: 0.0692
Epoch 26/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.0616 - val_loss: 0.0042
Epoch 27/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.0727 - val_loss: 0.2024
Epoch 28/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.1037 - val_loss: 0.0279
Epoch 29/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0304 - val_loss: 0.2230
Epoch 30/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 149ms/step - loss: 0.1096 - val_loss: 0.0072
Epoch 31/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0354 - val_loss: 0.2190
Epoch 32/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 146ms/step - loss: 0.0881 - val_loss: 0.0274
Epoch 33/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0598 - val_loss: 0.0046
Epoch 34/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0370 - val_loss: 0.6927
Epoch 35/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.4623 - val_loss: 0.0037
Epoch 36/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0153 - val_loss: 0.0721
Epoch 37/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0244 - val_loss: 0.0059
Epoch 38/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0316 - val_loss: 0.0050
Epoch 39/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 146ms/step - loss: 0.1076 - val_loss: 0.0626
Epoch 40/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0313 - val_loss: 0.0049
Epoch 41/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0164 - val_loss: 0.1659
Epoch 42/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0960 - val_loss: 0.0095
Epoch 43/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 146ms/step - loss: 0.0273 - val_loss: 0.0140
Epoch 44/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 148ms/step - loss: 0.0449 - val_loss: 0.0110
Epoch 45/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 146ms/step - loss: 0.0600 - val_loss: 0.0119
50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 44ms/step  
Sample raw predictions (after inverse transform and clipping): [0.3720941  0.5416374  0.835038   1.0700347  0.78572196]
RMSE =  1.7831588
Validation R-squared for item 1283: -0.011059045791625977
51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=2.3024468421936035
True y_val range (after inverse transform): min=0.0, max=13.0
No description has been provided for this image
-----------------------------------
Current item is  2141
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=3
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.0
x_eval_time shape before reshape: (1575, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1855, 14)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (163, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (175, 20)
Model: "sequential_174"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_349 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_254 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_350 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_255 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_173 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 18s 154ms/step - loss: 9047.7363 - val_loss: 252.6134
Epoch 2/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 452.2170 - val_loss: 10.1615
Epoch 3/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 37.2436 - val_loss: 1.7122
Epoch 4/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 8.6455 - val_loss: 0.7565
Epoch 5/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 4.2641 - val_loss: 0.3718
Epoch 6/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 2.4616 - val_loss: 1.3413
Epoch 7/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 1.9163 - val_loss: 0.0338
Epoch 8/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.8954 - val_loss: 0.0559
Epoch 9/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 154ms/step - loss: 0.4216 - val_loss: 0.0259
Epoch 10/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.2358 - val_loss: 0.0094
Epoch 11/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.2857 - val_loss: 0.1292
Epoch 12/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.2143 - val_loss: 0.0330
Epoch 13/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.0881 - val_loss: 0.1087
Epoch 14/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.1456 - val_loss: 0.0212
Epoch 15/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.1264 - val_loss: 0.1176
Epoch 16/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.1320 - val_loss: 0.1188
Epoch 17/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.1049 - val_loss: 0.0331
Epoch 18/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 0.0993 - val_loss: 0.3805
Epoch 19/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 0.2172 - val_loss: 0.0158
Epoch 20/100
107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.1209 - val_loss: 0.1267
51/51 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step  
Sample raw predictions (after inverse transform and clipping): [0.1080399  0.19204085 0.14207019 0.09809212 0.10098216]
RMSE =  0.31334543
Validation R-squared for item 2141: -0.19822299480438232
50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 42ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.939897060394287
True y_val range (after inverse transform): min=0.0, max=3.0
No description has been provided for this image
-----------------------------------
Current item is  2501
Data types of train_dummy after one-hot encoding:
id             object
item_id        object
dept_id        object
d              object
num_sold        int64
wm_yr_wk        int64
month           int64
year            int64
sell_price    float64
event_type    float64
store_id_1       bool
store_id_2       bool
store_id_3       bool
store_id_4       bool
state_id_1       bool
state_id_2       bool
state_id_3       bool
weekday_1        bool
weekday_2        bool
weekday_3        bool
weekday_4        bool
weekday_5        bool
weekday_6        bool
weekday_7        bool
snap_0           bool
snap_1           bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
       'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
       'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
      dtype='object')
Original y_data range: min=0, max=5
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8
x_eval_time shape before reshape: (1675, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1955, 14)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (208, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (215, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (156, 20)
store_x_eval.shape: (190, 20)
Model: "sequential_175"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_351 (LSTM)                      │ (None, 15, 256)             │         283,648 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_256 (Dropout)                │ (None, 15, 256)             │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_352 (LSTM)                      │ (None, 256)                 │         525,312 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_257 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_174 (Dense)                    │ (None, 1)                   │             257 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 809,217 (3.09 MB)
 Trainable params: 809,217 (3.09 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 156ms/step - loss: 15058.1328 - val_loss: 583.1071
Epoch 2/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 1731.4601 - val_loss: 13.6283
Epoch 3/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 182.6647 - val_loss: 58.2110
Epoch 4/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 91.0743 - val_loss: 19.8143
Epoch 5/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 58.1783 - val_loss: 23.0297
Epoch 6/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 35.0219 - val_loss: 10.2392
Epoch 7/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 25.0402 - val_loss: 3.9456
Epoch 8/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 17.6153 - val_loss: 2.8852
Epoch 9/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 13.1647 - val_loss: 3.6694
Epoch 10/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 9.9337 - val_loss: 2.1119
Epoch 11/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 9.3490 - val_loss: 0.6402
Epoch 12/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 7.2781 - val_loss: 1.6011
Epoch 13/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 5.1549 - val_loss: 0.7208
Epoch 14/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 4.6636 - val_loss: 2.0179
Epoch 15/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 4.0433 - val_loss: 1.7149
Epoch 16/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 3.3420 - val_loss: 0.6422
Epoch 17/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 2.9785 - val_loss: 0.6802
Epoch 18/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 2.6686 - val_loss: 0.2398
Epoch 19/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 2.0525 - val_loss: 0.7862
Epoch 20/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 1.7755 - val_loss: 1.1510
Epoch 21/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 1.8622 - val_loss: 0.4077
Epoch 22/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 1.3652 - val_loss: 0.2420
Epoch 23/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 1.8130 - val_loss: 0.1873
Epoch 24/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 1.0085 - val_loss: 0.2320
Epoch 25/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.9697 - val_loss: 0.2792
Epoch 26/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.8874 - val_loss: 0.3049
Epoch 27/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.7864 - val_loss: 0.0552
Epoch 28/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.9243 - val_loss: 0.0968
Epoch 29/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.8286 - val_loss: 0.0412
Epoch 30/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.5233 - val_loss: 0.1569
Epoch 31/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.5010 - val_loss: 0.1768
Epoch 32/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.3530 - val_loss: 0.0815
Epoch 33/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.3238 - val_loss: 0.0204
Epoch 34/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.4121 - val_loss: 0.1508
Epoch 35/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.4789 - val_loss: 0.1080
Epoch 36/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.2949 - val_loss: 0.0520
Epoch 37/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 169ms/step - loss: 0.2134 - val_loss: 0.0387
Epoch 38/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 166ms/step - loss: 0.3290 - val_loss: 0.0337
Epoch 39/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.2829 - val_loss: 0.0164
Epoch 40/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 1.5624 - val_loss: 0.1792
Epoch 41/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.3379 - val_loss: 0.0181
Epoch 42/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.2724 - val_loss: 0.0521
Epoch 43/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.1437 - val_loss: 0.0136
Epoch 44/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.0934 - val_loss: 0.0155
Epoch 45/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.1282 - val_loss: 0.0135
Epoch 46/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.1528 - val_loss: 0.0383
Epoch 47/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0777 - val_loss: 0.0201
Epoch 48/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0751 - val_loss: 0.0801
Epoch 49/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0832 - val_loss: 0.0945
Epoch 50/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.1012 - val_loss: 0.1727
Epoch 51/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.1368 - val_loss: 0.0126
Epoch 52/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0628 - val_loss: 0.0977
Epoch 53/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 0.0811 - val_loss: 0.0164
Epoch 54/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0467 - val_loss: 0.2808
Epoch 55/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.1174 - val_loss: 0.1855
Epoch 56/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.1716 - val_loss: 0.0173
Epoch 57/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0572 - val_loss: 0.0937
Epoch 58/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.1338 - val_loss: 0.0278
Epoch 59/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0759 - val_loss: 0.0452
Epoch 60/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 165ms/step - loss: 0.0722 - val_loss: 0.0126
Epoch 61/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 166ms/step - loss: 0.1084 - val_loss: 0.0143
Epoch 62/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 0.0367 - val_loss: 0.0145
Epoch 63/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.0761 - val_loss: 0.0124
Epoch 64/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.0377 - val_loss: 0.0439
Epoch 65/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.0676 - val_loss: 0.0476
Epoch 66/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.0875 - val_loss: 0.0173
Epoch 67/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.0408 - val_loss: 0.0185
Epoch 68/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0621 - val_loss: 0.0148
Epoch 69/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.0259 - val_loss: 0.0161
Epoch 70/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0430 - val_loss: 0.0131
Epoch 71/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0957 - val_loss: 0.0468
Epoch 72/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.0923 - val_loss: 0.0295
Epoch 73/100
106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.4627 - val_loss: 1.0215
51/51 ━━━━━━━━━━━━━━━━━━━━ 3s 48ms/step  
Sample raw predictions (after inverse transform and clipping): [0.24653293 0.3108156  0.4152503  0.47571957 0.43431592]
RMSE =  0.54304177
Validation R-squared for item 2501: 0.011475861072540283
53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 44ms/step
Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.6765174865722656
True y_val range (after inverse transform): min=0.0, max=4.0
No description has been provided for this image
-----------------------------------
Overall Validation MSE: 17.821836
Overall Validation RMSE: 4.2215915
Overall Validation MAE: 1.3745992
Overall Validation R-squared: -2.6647260009051483
In [41]:
y_val_pre_all = np.array(y_val_pre_all, dtype=object)
y_eval_pre_all = np.array(y_eval_pre_all, dtype=object)

def truncate_to_divisible(arr, divisor=28):
    if isinstance(arr, (list, np.ndarray)):
        current_size = len(arr)
        new_size = current_size - (current_size % divisor)
        return arr[:new_size]
    else:
        print(f"Warning: Skipping scalar element: {arr}")
        return np.array([]) # Return an empty array or handle as needed

y_val_output = [truncate_to_divisible(x).reshape(-1, 28) for x in y_val_pre_all if isinstance(x, (list, np.ndarray))]
y_eval_output = [truncate_to_divisible(x).reshape(-1, 28) for x in y_eval_pre_all if isinstance(x, (list, np.ndarray))]

# Check shapes
for i, arr in enumerate(y_val_output[:5]):
    print(f"Array {i}: shape {arr.shape}")
In [42]:
val_id_all = np.array(val_id_all)
eval_id_all = np.array(eval_id_all)

val_id_output = val_id_all.reshape(val_id_all.shape[0]*val_id_all.shape[1])
eval_id_output = eval_id_all.reshape(eval_id_all.shape[0]*eval_id_all.shape[1])
print(val_id_output.shape, eval_id_output.shape)
(1000,) (1000,)
In [43]:
y_val_output = [arr.flatten() for arr in y_val_output]

max_length = max(len(arr) for arr in y_val_output)
padded = [np.pad(arr, (0, max_length - len(arr)), 'constant') for arr in y_val_output]

y_val_output = np.vstack(padded)

if y_val_output.shape[1] % 28 != 0:
    padded_length = ((y_val_output.shape[1] + 27) // 28) * 28
    y_val_output = np.pad(y_val_output, ((0, 0), (0, padded_length - y_val_output.shape[1])), 'constant')
y_val_output = y_val_output.reshape(-1, 28)
num_samples = y_val_output.shape[0]

if len(val_id_output) > num_samples:
    val_id_output = val_id_output[:num_samples]
elif len(val_id_output) < num_samples:
    val_id_output = np.concatenate([val_id_output, np.full(num_samples - len(val_id_output), np.nan)])

output_cols = ['id'] + [f'F{i+1}' for i in range(28)]

val_df = pd.DataFrame(
    {'id': val_id_output},
    columns=['id'] + [f'F{i+1}' for i in range(28)]
)
for i in range(28):
    val_df[f'F{i+1}'] = y_val_output[:, i]

print(val_df.head())
print(f"Final shape: {val_df.shape}")
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[43], line 4
      1 y_val_output = [arr.flatten() for arr in y_val_output]
      3 # 2. Pad all arrays to max length
----> 4 max_length = max(len(arr) for arr in y_val_output)
      5 padded = [np.pad(arr, (0, max_length - len(arr)), 'constant') for arr in y_val_output]
      7 # 3. Stack into 2D array

ValueError: max() iterable argument is empty
In [106]:
y_eval_output = [np.array(arr).flatten() for arr in y_eval_output]

max_length = 0
if y_eval_output:
    max_length = max(len(arr) for arr in y_eval_output)

padded = [np.pad(arr, (0, max_length - len(arr)), 'constant') for arr in y_eval_output]

y_eval_output = np.vstack(padded) if padded else np.empty((0, 0))
if y_eval_output.shape[1] < 28:
    y_eval_output = np.pad(y_eval_output, ((0,0),(0,28-y_eval_output.shape[1])), 'constant')
elif y_eval_output.shape[1] > 28:
    y_eval_output = y_eval_output[:, :28]
num_samples = y_eval_output.shape[0]

if len(eval_id_output) != num_samples:
    print(f"Adjusting IDs: had {len(eval_id_output)}, need {num_samples}")
    if len(eval_id_output) > num_samples:
        eval_id_output = eval_id_output[:num_samples]
    else:
        placeholder_ids = [f"eval_{x}" for x in range(len(eval_id_output), num_samples)]
        eval_id_output = np.concatenate([eval_id_output, placeholder_ids])

eval_df = pd.DataFrame({'id': eval_id_output})
for i in range(28):
    eval_df[f'F{i+1}'] = y_eval_output[:, i]

if 'output_cols' in globals():
    eval_df = eval_df[output_cols]

val_df.to_csv("HOUSEHOLD_output_validation.csv", index =False)
eval_df.to_csv("HOUSEHOLD_output_evauation.csv", index =False)
Adjusting IDs: had 100, need 0
In [ ]: